Polarity semantics engine analytics platform

ABSTRACT

Embodiments of the systems and methods disclosed herein provide a prescriptive analytics platform, a polarity analysis engine, and a semantic analysis engine in which a user can identify a target objective and use the system to find out whether the user&#39;s objectives are being met, what predictive factors are positively or negatively affecting the targeted objectives, as well as what recommended changes the user can make to better meet the objectives. The systems and methods may include a polarity analysis engine configured to determine the polarity of terms in free-text input in view of the target objective and the predictive factors and use the polarity to generate the recommended changes. The systems and methods may also include a semantic analysis engine to extend the results of the polarity analysis engine for improved determination of predictive factors and improved recommendations.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/541,066, filed Aug. 14, 2019, which claims the benefit of U.S. PatentProvisional Application No. 62/886,564, filed Aug. 14, 2019, the entirecontent of which are hereby expressly incorporated by reference hereinin their entireties and for all purposes. In addition, any and allapplications for which a foreign or domestic priority claim isidentified in the application data sheet as filed with the presentapplication are also expressly incorporated by reference.

LIMITED COPYRIGHT AUTHORIZATION

A portion of the disclosure of this patent document includes materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightswhatsoever.

BACKGROUND OF THE DISCLOSURE

The systems and methods relate generally to the field of analyzingstructured and free text data sets.

SUMMARY OF EXAMPLE EMBODIMENTS

Various systems and methods for providing a prescriptive analytics andplatform are disclosed, which include a polarity analysis engineconfigured to determine the polarity of terms in free-text input in viewof a target objective and predictive factors and use the polarity togenerate recommended changes. The systems, methods, and devices of thedisclosure each have several innovative aspects, no single one of whichis solely responsible for the desirable attributes disclosed herein.

One embodiment of a polarity semantics engine analytics system isdisclosed. The polarity semantics engine analytics system may comprise:a first electronic database storing a set of response data, the set ofresponse data comprising a structured data set and free text data, andwherein the set of response data is based at least partly on anaggregated customer feedback data set; a second electronic databasestoring a first objective corresponding to the set of response data, thefirst objective selected by a user, wherein the first objective isassociated with one or more objective values; and a hardware processoris configured to execute computer-executable instructions in order to:access a correlated structured data set based at least in part on keypredictive factors that are correlated to the first objective andindicate one or more behavior patterns associated with the firstobjective; access a predictive model, wherein the predictive model isbased at least in part on the structured data set and the firstobjective, wherein the predictive model indicates one or more behaviorpatterns associated with an objective; automatically generate a firstelectronic graph data dependency structure based at least in part on thepredictive model, wherein the first electronic graph data dependencystructure represents relationships among at least a portion of variablesof the correlated structured data set, and wherein the relationships arebased at least in part on the strength of an association among each ofthe variables of the correlated structured data set; access polarityvalues associated with at least a portion of the free text data set;automatically generate a second electronic graph data dependencystructure of associations among model inputs and polarity valuesassociated with at least a portion of the free text data, wherein thepolarity values indicate that the associated free text data isassociated with a degree of impact on one or more outcomes;automatically generate an extended electronic graph data dependencystructure based at least in part on the first and second electronicgraph data dependency structures; and automatically generate arecommendation action based at least in part on the extended electronicgraph data dependency structure. Also, the system's hardware processorcan be configured to execute computer-executable instructions in orderto determine associative or causal interpretations of discoveredrelationships between each of the variables of the correlated structureddata set; generate a data packet that includes a graphicalrepresentation of at least a subset of the polarity values that includesa graphical representation of the extended graph, the data packetconfigured for display on a remote computing device; generate a datapacket that includes a graphical representation of at least a subset ofthe polarity values that includes a graphical representation of whethera term in the at least a subset is tied to a negative sentiment or apositive sentiment, the data packet configured for display on a remotecomputing device; generate a data packet that includes a graphicalrepresentation of at least a subset of the polarity values that includesa graphical representation of the frequency and strength of terms in thesubset, the data packet configured for display on a remote computingdevice; or generate a data packet that includes a graphicalrepresentation of at least a subset of the polarity values that includesa graphical representation of whether a term in the subset is tied to anegative sentiment or a positive sentiment and the frequency andstrength of terms in the subset, the data packet configured for displayon a remote computing device. Also, the recommendation can be based atleast in part on the associative or causal interpretations.

One embodiment of a computer-implemented method related to a polaritysemantics engine analytics system is disclosed. The computer-implementedmethod comprising: accessing from a first electronic database a set ofresponse data, the set of response data comprising a structured data setand a free text data set, and wherein the set of response data is basedat least partly on an aggregated customer feedback data set; accessingfrom a second electronic database a first objective corresponding to theset of response data, the first objective is associated with one or moreobjective values; accessing a correlated structured data set based atleast in part on key predictive factors that are correlated to the firstobjective and indicate one or more behavior patterns associated with thefirst objective; accessing a predictive model, wherein the predictivemodel is based at least in part on the structured data set and the firstobjective, wherein the predictive model indicates one or more behaviorpatterns associated with an objective; automatically generating a firstelectronic graph data dependency structure based at least in part on thepredictive model, wherein the first electronic graph data dependencystructure represents relationships among at least a portion of variablesof the correlated structured data set, and wherein the relationships arebased at least in part on the strength of an association among each ofthe variables of the correlated structured data set; accessing polarityvalues associated with at least a portion of the free text data set;automatically generating a second electronic graph data dependencystructure of associations among model inputs and polarity valuesassociated with at least a portion of the free text data, wherein thepolarity values indicate that the associated free text data isassociated with a degree of impact on one or more outcomes;automatically generating an extended electronic graph data dependencystructure based at least in part on the first and second electronicgraph data dependency structures; and automatically generating arecommendation action based at least in part on the extended electronicgraph data dependency structure. Also, the computer-implemented methodand further comprise: determining associative or causal interpretationsof discovered relationships between each of the variables of thecorrelated structured data set; generating a data packet that includes agraphical representation of at least a subset of the polarity valuesthat includes a graphical representation of the extended graph, the datapacket configured for display on a remote computing device; generating adata packet that includes a graphical representation of at least asubset of the polarity values that includes a graphical representationof whether a term in the at least a subset is tied to a negativesentiment or a positive sentiment, the data packet configured fordisplay on a remote computing device; generating a data packet thatincludes a graphical representation of at least a subset of the polarityvalues that includes a graphical representation of the frequency andstrength of terms in the subset, the data packet configured for displayon a remote computing device; or generating a data packet that includesa graphical representation of at least a subset of the polarity valuesthat includes a graphical representation of whether a term in the subsetis tied to a negative sentiment or a positive sentiment and thefrequency and strength of terms in the subset, the data packetconfigured for display on a remote computing device. Also, therecommendation can be based at least in part on the associative orcausal interpretations.

One embodiment of a non-transitory computer storage having storedthereon a computer program, the computer program including executableinstructions that instruct a computer system is disclosed. Thenon-transitory computer storage may instruct a computer system to atleast: access from a first electronic database a set of response data,the set of response data comprising a structured data set and a freetext data set, and wherein the set of response data is based at leastpartly on an aggregated customer feedback data set; access from a secondelectronic database a first objective corresponding to the set ofresponse data, the first objective is associated with one or moreobjective values; access a correlated structured data set based at leastin part on key predictive factors that are correlated to the firstobjective and indicate one or more behavior patterns associated with thefirst objective; access a predictive model, wherein the predictive modelis based at least in part on the structured data set and the firstobjective, wherein the predictive model indicates one or more behaviorpatterns associated with an objective; automatically generate a firstelectronic graph data dependency structure based at least in part on thepredictive model, wherein the first electronic graph data dependencystructure represents relationships among at least a portion of variablesof the correlated structured data set, and wherein the relationships arebased at least in part on the strength of an association among each ofthe variables of the correlated structured data set; access polarityvalues associated with at least a portion of the free text data set;automatically generate a second electronic graph data dependencystructure of associations among model inputs and polarity valuesassociated with at least a portion of the free text data, wherein thepolarity values indicate that the associated free text data isassociated with a degree of impact on one or more outcomes;automatically generate an extended electronic graph data dependencystructure based at least in part on the first and second electronicgraph data dependency structures; and automatically generate arecommendation action based at least in part on the extended electronicgraph data dependency structure. Also, the system's hardware processorcan be configured to execute computer-executable instructions in orderto determine associative or causal interpretations of discoveredrelationships between each of the variables of the correlated structureddata set; generate a data packet that includes a graphicalrepresentation of at least a subset of the polarity values that includesa graphical representation of the extended graph, the data packetconfigured for display on a remote computing device; generate a datapacket that includes a graphical representation of at least a subset ofthe polarity values that includes a graphical representation of whethera term in the at least a subset is tied to a negative sentiment or apositive sentiment, the data packet configured for display on a remotecomputing device; generate a data packet that includes a graphicalrepresentation of at least a subset of the polarity values that includesa graphical representation of the frequency and strength of terms in thesubset, the data packet configured for display on a remote computingdevice; or generate a data packet that includes a graphicalrepresentation of at least a subset of the polarity values that includesa graphical representation of whether a term in the subset is tied to anegative sentiment or a positive sentiment and the frequency andstrength of terms in the subset, the data packet configured for displayon a remote computing device. Also, the recommendation can be based atleast in part on the associative or causal interpretations.

One embodiment of a prescriptive analytics system for conducting apolarity analysis on unstructured, free text data is disclosed. Theprescriptive analytics system may comprise: a first electronic databasestoring a set of response data, the set of response data comprising astructured data set and a free text data set; a second electronicdatabase storing a first objective corresponding to the set of responsedata, the first objective selected by a user; a hardware processor isconfigured to execute computer-executable instructions in order to:access from the first electronic database the set of response data;analyze the structured data set to identify key predictive factorswithin the structured data set that have a statistically significantcorrelation to the first objective; generate a correlated structureddata set from the structured data set using the identified keypredictive factors; convert terms within the free text data set intotheir lemma standard form to generate a standardized lemma data set;filter the standardized lemma data set to remove terms that are notassociated with actionable words to create a filtered lemma data set;aggregate the filtered lemma data set with the correlated structureddata set; generate scores for the filtered lemma data set in view of thefirst objective to create a scored lemma data set; analyze thedistribution of the scored lemma data set against a distribution of thefirst objective in the correlated structured data set; balance thedistribution of the scored lemma data set against rarity and aggregatefrequency of terms in the scored lemma data set to create a distributedlemma data set; and assign polarity values to terms in the distributedlemma data set, wherein the polarity values indicate whether each of theterms is a positive term or a negative term.

One embodiment of a computer-implemented method of conducting a polarityanalysis on unstructured, free text data is disclosed. Thecomputer-implemented method may comprise: as implemented by one or morecomputing devices configured with specific computer-executableinstructions, accessing from a first electronic database a set ofresponse data that includes a structured data set and an unstructureddata set; analyzing the structured data set to identify key predictivefactors within the structured data set that have a statisticallysignificant correlation to the first objective; generating a correlatedstructured data set from the structured data set using the identifiedkey predictive factors; converting terms within the free text data setinto their lemma standard form to generate a standardized lemma dataset; filtering the standardized lemma data set to remove terms that arenot associated with actionable words to create a filtered lemma dataset; aggregating the filtered lemma data set with the correlatedstructured data set; generating scores for the filtered lemma data setin view of the first objective to create a scored lemma data set;analyzing the distribution of the scored lemma data set against adistribution of the first objective in the correlated structured dataset; balancing the distribution of the scored lemma data set againstrarity and aggregate frequency of terms in the scored lemma data set tocreate a distributed lemma data set; and assigning polarity values toterms in the distributed lemma data set, wherein the polarity valuesindicate whether each of the terms is a positive term or a negativeterm.

One embodiment of a non-transitory computer storage having storedthereon a computer program, the computer program including executableinstructions that instruct a computer system is disclosed. Thenon-transitory computer storage may instruct a computer system to atleast: access from a first electronic database a set of response datathat includes a structured data set and an unstructured data set;analyze the structured data set to identify key predictive factorswithin the structured data set that have a statistically significantcorrelation to the first objective; generate a correlated structureddata set from the structured data set using the identified keypredictive factors; convert terms within the free text data set intotheir lemma standard form to generate a standardized lemma data set;filter the standardized lemma data set to remove terms that are notassociated with actionable words to create a filtered lemma data set;aggregate the filtered lemma data set with the correlated structureddata set; generate scores for the filtered lemma data set in view of thefirst objective to create a scored lemma data set; analyze thedistribution of the scored lemma data set against a distribution of thefirst objective in the correlated structured data set; balance thedistribution of the scored lemma data set against rarity and aggregatefrequency of terms in the scored lemma data set to create a distributedlemma data set; and assign polarity values to terms in the distributedlemma data set, wherein the polarity values indicate whether each of theterms is a positive term or a negative term.

In some embodiments, there can be a prescriptive analytics systemcomprising: a first electronic database storing a set of response data,the set of response data comprising a structured data set and a freetext data set, and wherein the set of response data is based at leastpartly on an aggregated customer feedback data set; a second electronicdatabase storing a first objective corresponding to the set of responsedata, the first objective selected by a user, wherein the firstobjective is associated with one or more objective values such that eachobjective value is associated with a degree of impact on one or moreoutcomes; a hardware processor is configured to executecomputer-executable instructions in order to: access from the firstelectronic database the set of response data; access from the secondelectronic database the first objective; generate a predictive modelbased at least in part on the structured data set and the firstobjective, wherein the predictive model indicates one or more behaviorpatterns associated with an objective; analyze the structured data setto identify key predictive factors within the structured data set thatare correlated to the first objective and indicate one or more behaviorpatterns associated with the first objective; generate a correlatedstructured data set from the structured data set using the identifiedkey predictive factors; convert a plurality of terms within the freetext data set into a plurality of lemmas corresponding to the pluralityof terms; analyze the plurality of lemmas to determine: a first numberof occurrences of lemmas associated with a positive objective value andbased on the first objective; and a second number of occurrences oflemmas associated with a negative objective value and based on the firstobjective; assign polarity values to terms in the plurality of lemmasbased at least in part on the first objective and the number ofoccurrences of lemmas associated with a positive objective value ornegative objective value, wherein the polarity values indicate whethereach of the terms is a positive term or a negative term based at leastpartly on a determination of the terms as being associated with a degreeof impact on one or more outcomes; generate a first graph ofrelationships among at least a portion of the variables of thecorrelated structured data set, wherein the relationships are based atleast in part on the strength of an association among each of thevariables of the correlated structured data set; generate a second graphof associations among model inputs and at least a portion of theplurality of lemmas; generate an extended graph based at least in parton the first and second graphs; and generate a recommendation actionbased at least in part on the extended graph.

In some embodiments, the hardware processor of the prescriptiveanalytics system can be configured to execute computer-executableinstructions in order to determine associative or causal interpretationsof discovered structural relationships between each of the variables ofthe structured data set based at least in part on the plurality oflemmas; to generate recommendations for achieving an improvement to thefirst objective using the extended graph; to generate a data packet thatincludes a graphical representation of at least a subset of theplurality of lemmas that includes a graphical representation of theextended graph, the data packet configured for display on a remotecomputing device; to generate a data packet that includes a graphicalrepresentation of at least a subset of the plurality of lemmas thatincludes a graphical representation of whether a term in the at least asubset is tied to a negative sentiment or a positive sentiment, the datapacket configured for display on a remote computing device; to generatea data packet that includes a graphical representation of at least asubset of the plurality of lemmas that includes a graphicalrepresentation of the frequency and strength of terms in the subset, thedata packet configured for display on a remote computing device; or togenerate a data packet that includes a graphical representation of atleast a subset of the plurality of lemmas that includes a graphicalrepresentation of whether a term in the subset is tied to a negativesentiment or a positive sentiment and the frequency and strength ofterms in the subset, the data packet configured for display on a remotedevice. In some embodiments, the recommendation can be based at least inpart on the associative or causal interpretations

In some embodiments, there can be a computer-implemented methodcomprising: accessing from a first electronic database a set of responsedata, wherein the set of response data comprising a structured data setand a free text data set, and wherein the set of response data is basedat least partly on an aggregated customer feedback data set; accessingfrom a second electronic database a first objective, wherein the firstobjective corresponds to the set of response data, and wherein the firstobjective is associated with one or more objective values such that eachobjective value is associated with a degree of impact on one or moreoutcomes; generating a predictive model based at least in part on thestructured data set and the first objective, wherein the predictivemodel indicates one or more behavior patterns associated with anobjective; analyzing the structured data set to identify key predictivefactors within the structured data set that are correlated to the firstobjective and indicate one or more behavior patterns associated with thefirst objective; generating a correlated structured data set from thestructured data set using the identified key predictive factors;converting a plurality of terms within the free text data set into aplurality of lemmas corresponding to the plurality of terms; analyzingthe plurality of lemmas to determine: a first number of occurrences oflemmas associated with a positive objective value and based on the firstobjective; and a second number of occurrences of lemmas associated witha negative objective value and based on the first objective; assigningpolarity values to terms in the plurality of lemmas based at least inpart on the first objective and the number of occurrences of lemmasassociated with a positive objective value or negative objective value,wherein the polarity values indicate whether each of the terms is apositive term or a negative term based at least partly on adetermination of the terms as being associated with a degree of impacton one or more outcomes; generating a first graph of relationships amongat least a portion of the variables of the correlated structured dataset, wherein the relationships are based at least in part on thestrength of an association among each of the variables of the correlatedstructured data set; generating a second graph of associations amongmodel inputs and at least a portion of the plurality of lemmas;generating an extended graph based at least in part on the first andsecond graphs; and generating a recommendation action based at least inpart on the extended graph.

In some embodiments, the computer-implemented method can furthercomprise: determining associative or causal interpretations ofdiscovered structural relationships between each of the variables of thestructured data set based at least in part on the plurality of lemmas;generating recommendations for achieving an improvement to the firstobjective using the extended graph; generating a data packet thatincludes a graphical representation of at least a subset of theplurality of lemmas that includes a graphical representation of theextended graph, the data packet configured for display on a remotecomputing device; generating a data packet that includes a graphicalrepresentation of at least a subset of the plurality of lemmas thatincludes a graphical representation of whether a term in the at least asubset is tied to a negative sentiment or a positive sentiment, the datapacket configured for display on a remote computing device; generating adata packet that includes a graphical representation of at least asubset of the plurality of lemmas that includes a graphicalrepresentation of the frequency and strength of terms in the subset, thedata packet configured for display on a remote computing device; orgenerating a data packet that includes a graphical representation of atleast a subset of the plurality of lemmas that includes a graphicalrepresentation of whether a term in the subset is tied to a negativesentiment or a positive sentiment and the frequency and strength ofterms in the subset, the data packet configured for display on a remotedevice. In some embodiments, the recommendation can be based at least inpart on the associative or causal interpretations.

In some embodiments, there can be a non-transitory computer storagehaving stored thereon a computer program, the computer program includingexecutable instructions that instruct a computer system to at least: afirst electronic database storing a set of response data, the set ofresponse data comprising a structured data set and a free text data set,and wherein the set of response data is based at least partly on anaggregated customer feedback data set; a second electronic databasestoring a first objective corresponding to the set of response data, thefirst objective selected by a user, wherein the first objective isassociated with one or more objective values such that each objectivevalue is associated with a degree of impact on one or more outcomes; ahardware processor is configured to execute computer-executableinstructions in order to: access from the first electronic database theset of response data; access from the second electronic database thefirst objective; generate a predictive model based at least in part onthe structured data set and the first objective, wherein the predictivemodel indicates one or more behavior patterns associated with anobjective; analyze the structured data set to identify key predictivefactors within the structured data set that are correlated to the firstobjective and indicate one or more behavior patterns associated with thefirst objective; generate a correlated structured data set from thestructured data set using the identified key predictive factors; converta plurality of terms within the free text data set into a plurality oflemmas corresponding to the plurality of terms; analyze the plurality oflemmas to determine: a first number of occurrences of lemmas associatedwith a positive objective value and based on the first objective; and asecond number of occurrences of lemmas associated with a negativeobjective value and based on the first objective; assign polarity valuesto terms in the plurality of lemmas based at least in part on the firstobjective and the number of occurrences of lemmas associated with apositive objective value or negative objective value, wherein thepolarity values indicate whether each of the terms is a positive term ora negative term based at least partly on a determination of the terms asbeing associated with a degree of impact on one or more outcomes;generate a first graph of relationships among at least a portion of thevariables of the correlated structured data set, wherein therelationships are based at least in part on the strength of anassociation among each of the variables of the correlated structureddata set; generate a second graph of associations among model inputs andat least a portion of the plurality of lemmas; generate an extendedgraph based at least in part on the first and second graphs; andgenerate a recommendation action based at least in part on the extendedgraph.

In some embodiments, the non-transitory computer storage can be furtherconfigured to: determine associative or causal interpretations ofdiscovered structural relationships between each of the variables of thestructured data set based at least in part on the plurality of lemmas;to generate recommendations for achieving an improvement to the firstobjective using the extended graph; to generate a data packet thatincludes a graphical representation of at least a subset of theplurality of lemmas that includes a graphical representation of theextended graph, the data packet configured for display on a remotecomputing device; to generate a data packet that includes a graphicalrepresentation of at least a subset of the plurality of lemmas thatincludes a graphical representation of whether a term in the at least asubset is tied to a negative sentiment or a positive sentiment, the datapacket configured for display on a remote computing device; to generatea data packet that includes a graphical representation of at least asubset of the plurality of lemmas that includes a graphicalrepresentation of the frequency and strength of terms in the subset, thedata packet configured for display on a remote computing device; or togenerate a data packet that includes a graphical representation of atleast a subset of the plurality of lemmas that includes a graphicalrepresentation of whether a term in the subset is tied to a negativesentiment or a positive sentiment and the frequency and strength ofterms in the subset, the data packet configured for display on a remotedevice. In some embodiments, the recommendation can be based at least inpart on the associative or causal interpretations

In some embodiments, a computer-implemented method can comprise: asimplemented by one or more computing devices configured with specificcomputer-executable instructions, accessing from an electronic databasea set of response data that includes a structured data set and anunstructured data set, wherein the set of response data is based atleast partly on an aggregated customer feedback dataset; analyzing thestructured data set to identify key predictive factors within thestructured data set that are correlated to a first objective andindicate one or more behavior patterns associated with the firstobjective; generating a correlated structured data set from thestructured data set using the identified key predictive factors;converting a plurality of terms within the free text data set into aplurality of lemmas corresponding to the plurality of terms; filteringthe plurality of lemmas to remove terms that are not associated withnouns and adjectives to create a filtered lemma data set; aggregatingthe filtered lemma data set with the correlated structured data set;generating scores for the filtered lemma data set based at least in parton the first objective to create a scored lemma data set; analyzing theplurality of lemmas to determine: a first number of occurrences oflemmas associated with a positive objective value; and a second numberof occurrences of lemmas associated with a negative objective value;balancing the distribution of the scored lemma data set against rarityand aggregate frequency of terms in the scored lemma data set to createa distributed lemma data set; and assigning polarity values to terms inthe distributed lemma data set, wherein the polarity values indicatewhether each of the terms is a positive term or a negative term, andbased at least partly on a determination of the terms as beingassociated with a degree of impact on one or more outcomes; generating arecommendation action based at least in part on the assigned polarityvalues.

Although certain embodiments and examples are disclosed herein,inventive subject matter extends beyond the examples in the specificallydisclosed embodiments to other alternative embodiments and/or uses, andto modifications and equivalents thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawings will be provided by the Office upon request and paymentof the necessary fee.

The foregoing aspects and many of the attendant advantages of thisdisclosure will become more readily appreciated as the same becomebetter understood by reference to the following detailed description,when taken in conjunction with the accompanying drawings. Theaccompanying drawings, which are incorporated in, and constitute a partof, this specification, illustrate embodiments of the disclosure.

Throughout the drawings, reference numbers are re-used to indicatecorrespondence between referenced elements. The drawings are provided toillustrate embodiments of the subject matter described herein and not tolimit the scope thereof.

FIG. 1A is a block diagram of a computing environment that includes aUser Device, a Third Party Data Collection System, and a PrescriptiveAnalytics System, according to one embodiment.

FIG. 1B is a block diagram showing the embodiment of FIG. 1A and anexemplary data flow among the User Device, the Third Party DataCollection System, and the Prescriptive Analytics System, according toone embodiment.

FIG. 2 is a block diagram showing one embodiment in which PrescriptiveAnalytics System and its components process inputs and provide outputs.

FIG. 3 is a block diagram which illustrates a logical flow diagram forone embodiment of an example process for conducting a polarity analysis.

FIG. 4 schematically illustrates a logical flow diagram for oneembodiment of an example process for performing one feature of thePrescriptive Analytics System to generate and provide a list ofrecommendations to achieve a designated amount to change an objective.

FIG. 5 schematically illustrates a logical flow diagram for oneembodiment of an example process for processing free text data.

FIG. 6 schematically illustrates a logical flow diagram for oneembodiment of an example process for analyzing and processing data andgenerating recommendations.

FIG. 7 is a block diagram showing one embodiment of an example processfor analyzing key predictive factors, a focused objective delta, andfree text data to generate a graphical representation of words based ontheir predictive nature and polarities.

FIG. 8 is a screen shot depicting one embodiment of an example userinterface that generates a graphical depiction of various datasetsassociated with a specific account.

FIG. 9 is a screen shot depicting one embodiment of an example userinterface, allowing for uploading data or directing the system to aremote location where data is located and available for the system todownload.

FIG. 10 is a screen shot depicting one embodiment of an example userinterface, displaying the details of data automatically classified, butalso allowing for additional classification and customization as well asthe creation and utilization of a schema.

FIG. 11 is a screen shot depicting one embodiment of an example userinterface, allowing for the input of pre-set criteria to analyze datawith additional customizations.

FIG. 12 is a screen shot depicting one embodiment of an example userinterface that generates a graphical depiction of the results of ananalysis.

FIG. 13A is a screen shot depicting one embodiment of an example userinterface, allowing for the setting of a goal based on focused elementsof data and recommendations based on set configuration.

FIG. 13B is a screen shot depicting one embodiment of an example userinterface, similar to FIG. 13A, showing multiple possiblerecommendations to achieve the goal set out.

FIG. 13C is a screen shot depicting one embodiment of an example userinterface, similar to FIG. 13B, showing a word cloud representing thepredictive nature of the language and sentiment pertaining to a specificrecommendation.

FIG. 14 is a screen shot depicting one embodiment of an example userinterface that generates a graphical depiction of the results of ananalysis with respect to promoters related to set criteria.

FIG. 15 is a screen shot depicting one embodiment of an example userinterface that generates a graphical depiction of predictive details ofan analysis pertaining to promoters, also illustrated by an image wherecertain data points hold more or less predictive value based on colorand combination.

FIG. 16 is a screen shot depicting one embodiment of an example userinterface that generates a graphical depiction of specific profiles ofmost positive and most negative characteristics of individuals in a dataset pertaining to the promoter analysis.

FIG. 17 is a screen shot depicting one embodiment of an example userinterface that generates a graphical depiction of how data correlateswith a positive or negative influence on an objective.

FIG. 18 is a screen shot depicting one embodiment of an example userinterface that generates a graphical depiction of how data combinationscorrelate with a positive or negative influence on an objective.

FIG. 19 is a screen shot depicting one embodiment of an example userinterface that generates a graphical depiction of a relationship betweendata points and how they lead to an objective pertaining to the promoteranalysis.

FIG. 20 is a screen shot depicting one embodiment of an example userinterface that generates a graphical depiction of the results of ananalysis with respect to detractors related to criteria set.

FIG. 21 is a screen shot depicting one embodiment of an example userinterface that generates a graphical depiction of predictive details ofan analysis pertaining to detractors, also showing where data holds moreor less predictive value based on color and combination.

FIG. 22 is a screen shot depicting one embodiment of an example userinterface that generates a graphical depiction of specific profiles ofmost positive and most negative characteristics of individuals in a dataset pertaining to the detractor analysis.

FIG. 23 is a screen shot depicting one embodiment of an example userinterface, similar to FIG. 19 , where the browser window shows arelationship between data and how they lead to an objective pertainingto the detractor analysis.

FIG. 24 is a screen shot depicting one embodiment of an example userinterface, similar to FIG. 11 , allowing for the input of any customcriteria to analyze data with additional customizations.

FIG. 25 is a screen shot depicting one embodiment of an example userinterface that generates a graphical depiction of the summary ofpredictive results based on inputs.

FIG. 26 is a screen shot depicting one embodiment of an example userinterface, similar to FIG. 15 , showing predictive details of ananalysis, also showing where data holds more or less predictive valuebased on color and combination.

FIG. 27 is a screen shot depicting one embodiment of an example userinterface, similar to FIG. 16 , showing specific profiles of mostpositive and most negative characteristics of individuals in a data setpertaining to the promoter analysis.

FIG. 28 is a screen shot depicting one embodiment of an example userinterface, similar to FIG. 19 , showing the relationship between datapoints and how they lead to an objective.

FIG. 29 is a screen shot depicting one embodiment of an example userinterface, similar to FIG. 13A, allowing for the setting of a goal basedon focused elements of data and recommendations based on aconfiguration.

FIG. 30A is a screen shot depicting one embodiment of an example userinterface, allowing for the entry of a single prediction based oncustomizable criteria and a previously generated model and renderingresults of each prediction, including precedence of contributors toprediction.

FIG. 30B is a screen shot depicting one embodiment of an example userinterface, allowing for the entry of bulk data point prediction based oncustomizable criteria and a previously generated model.

FIG. 30C is a screen shot depicting one embodiment of an example userinterface, allowing for the entry of additional data to test or retraina prediction model.

FIG. 31A is a block diagram of a computing environment that includes adatabase, a dependence circuit, and a semantic circuit, according to oneembodiment.

FIG. 31B is a block diagram showing the embodiment of FIG. 31A and anexemplary data flow among the database, a dependence circuit, and asemantic circuit, according to one embodiment.

FIG. 32A is a block diagram showing one embodiment in which aprescriptive analytics system and its components, which includes asemantic analytics engine, process inputs and provide outputs.

FIG. 32B is a block diagram showing one embodiment of a semanticsanalytics engine and its components, which includes an input relationgraph generation system, a relation repository, an input/lemma relationgraph generation system, a connection system, and an interpreter system.

FIG. 33 is a block diagram which illustrates a logical flow diagram forone embodiment of an example process for conducting a polarity analysisenhanced with a relation module and a semantic analysis.

FIG. 34 schematically illustrates a logical flow diagram for oneembodiment of an example process for analyzing and processing data andgenerating recommendations using a polarity analysis enhanced with arelation module and a semantic analysis.

FIG. 35A is a screen shot depicting one embodiment of a generated graph,illustrating a graph generated by a process using a dependence circuit.

FIG. 35B is a screen shot depicting one embodiment of an generatedgraph, illustrating a graph generated by a process using a semanticcircuit based on FIG. 35A.

FIG. 36 is a block diagram showing one embodiment of a PrescriptiveAnalytics System in communication with a network and various systems,such as websites and/or online services.

DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS

Embodiments of the disclosure will now be described with reference tothe accompanying figures. The terminology used in the descriptionpresented herein is not intended to be interpreted in any limited orrestrictive manner, simply because it is being utilized in conjunctionwith a detailed description of embodiments of the disclosure.Furthermore, embodiments of the disclosure may include several novelfeatures, no single one of which is solely responsible for its desirableattributes or which is essential to practicing the embodiments of thedisclosure herein described. For purposes of this disclosure, certainaspects, advantages, and novel features of various embodiments aredescribed herein.

I. Overview

The growth and collection of big data has greatly increased theopportunities for businesses to gain better insight as to their businessoperations. However, extracting accurate and meaningful insight fromthese large data sets can be challenging as many systems for analyzingbig data of have one or more of the following shortcomings, (1) they arenot comprehensive and lead to inaccurate prediction models, (2) they areinefficient and lead to high processing times, (3) they present theinformation and results in a complex and confusing manner, (4) they lackof customizability, (5) they are unable to adapt and change theprediction model in view of constantly changing data, and/or (6) theyrequire data scientist/modeling expertise. An additional problem is theyare unable to provide meaningful, computational analysis of unstructureddata, even though the unstructured data often includes insightfulopinions about key items of interest to the user, such as a topic,product, company, employee base, or event.

The systems and methods disclosed herein provide a prescriptiveanalytics platform and polarity analysis engine in which a user canidentify a target objective and use the system to find out whether theuser's objectives are being met, what factors are positively ornegatively affecting the targeted objectives, as well as what changesthe user can make to better meet the objectives. In some embodiments,the user can upload a large set of survey data that includes bothstructured and unstructured data and select a target objective tied tothe survey data. A predictive analytics engine correlates the structureddata with the selected objective to generate correlated structured datathat indicates which factors within the structured data are the mostpredictive for the objective. It is recognized that a wide variety ofpredictive analytics engines may be used to generate the correlatedstructured data, including, for example, the predictive analytics engineprovided by Compellon, Inc. Embodiments of the polarity analysis enginethen analyze the free text data input and correlated structured data toderive polarity values for terms within the free text data in view ofthe selected objective. The polarity analysis engine is able to identifycommonly used terms that negatively affect the selected objective and/orcommonly used terms that positively affect the selected objective. Agraphical display module may then analyze the results from the polarityanalysis engine to generate a graphical representation of these termsalong with indicators of their corresponding polarity for presentationto the user. The platform may also utilize the polarity analyses togenerate recommended actions the user can take to maintain the positivesentiment and/or address the causes of the negative sentiment. Sentimentof a word or term includes indicators of the author's opinion expressedtowards the related topic.

A benefit of some embodiments is that the prescriptive analyticsplatform and polarity analysis engine presents a polarity analysis ofunstructured data in an easily digestible graphic representation, thusallowing the user, a human operator who is not an expert in data scienceor predictive modeling, to quickly understand key sentiment data driverswithin large sets of unstructured data.

In some embodiments, the prescriptive analytics platform or system, asdescribed in more detail herein, uses a mix of structured andnon-structured data for generating prescriptions that can be used todetermine whether a preconfigured objective is being met using twocircuits. In a dependence circuit, the structured part of the data isused to discover essential relations among data elements, to build apredictor of outcomes, and to generate quantitative requirements to beincluded in a prescription. In a semantic circuit, the elements ofnon-structured (for example, natural language) data are classified as“positive” or “negative” by their corresponding association withpositive or negative business outcomes predicted in the dependencecircuit. The language elements may be classified as positive or negativein terms of the specific preconfigured objective the language elementsrepresent. As a result, the final prescription regarding thepreconfigured objective can be enriched with variants of concreteimprovement actions. In some embodiments, the prescription generated isconcrete in pointing to the advised practical beneficial changes and/orin an assessment to what degree those changes would be sufficient withrespect to the preconfigured objective. Various embodiments of aprescriptive analytics platform or system can be found in U.S. Pat. No.10,235,336, issued Mar. 19, 2019, the entire contents of which arehereby expressly incorporated by reference herein in their entirety andfor all purposes.

In some embodiments, the prescriptive analytics platform or system candetermine which parts of inputted data are relevant, redundant, orrelated, as well as which data variables, or combinations of variables,are the most to least predictive based on one or more complexstatistical analyses. The system may generate a graph of relations amongeach of the variables of the structured part. Links in the graph can bebuilt accordingly to strong associations among variables and represent adependence circuit modeling the structural part. Also, the dependencecircuit can be extended with additional links to natural, strong-relatedlanguage elements. In some embodiments, language elements may beclassified as related to positive and negative predictions, and may becharacterized in a more specific and granular way as a concretespecification of context, from which the dependence circuit initiallyarose. The extended graph can be referred to as a semantic circuit. Itcan be used for more specific advice of action and for causal analysisof events based on a preconfigured objective.

II. Overview of A Computing Environment

In some embodiments, a prescriptive analytics platform or system (“PAS”)is included within a computing environment that allows a human operator(for example, a business owner) to interface with the prescriptiveanalytics platform via a user device or system. The prescriptiveanalytics system may communicate with a database that stores large datasets related to the operator (for example, survey data about theoperator's business products or employees). The operator may upload thelarge data sets to the database, or the large data sets may be added tothe database by third parties that collect data on behalf of theoperator (for example, a survey company that collects survey data aboutthe operator's business products or employees). The PAS can then receiveor access the data sets from the database. The operator may utilize anapplication interface provided by the PAS to instruct the PAS toretrieve or upload one or more of the data sets stored in the database.The operator may also use the application interface to select a targetobjective. The PAS then determines statistically significantcorrelations between objective and structured data within the data setthat are the key predictive drivers. The PAS may then present a reportof these identified drivers in the application interface for review bythe operator. The PAS may also correlate unstructured data within thedata set with the correlated structured data and then conduct a polarityanalysis of the unstructured data to identify commonly used terms in theunstructured data that correspond to a positive, neutral, or negativesentiment related to the key predictive drivers. Structured dataincludes organized data that follows a pre-defined data model (forexample, labeled data), and unstructured data includes data that is notorganized in a pre-defined manner (for example, unlabeled data) or doesnot have a pre-defined data model. Typically, unstructured data includesa lot of textual information, but the data may also include numbers,dates, and other types of information. Free text data, or free-form textdata, is a type of unstructured data that includes mostly textualinformation. The PAS may present a graphical representation of thepreviously identified commonly used terms in the unstructured data thatcorrespond to a positive, neutral, or negative sentiment related to thekey predictive drivers that also depicts their corresponding polarity.The PAS may also use the polarity analysis to automatically generaterecommended actions for achieving changes designated by the operator.

FIG. 1A is a block diagram depicting one embodiment of an architecture100 for analyzing collections of structured and non-structured data by aPrescriptive Analytics System (“PAS”). The architecture 100 shown inFIGS. 1A and 1B include user device 104 accessed by an operator or userof the PAS 108, feedback data 102 or data sets from the user's customersor employees, a third party data collection system 106 with a database107, a PAS 108, and an application interface (sometimes referred to asan interface) 110. The exemplary PAS 108 includes a predictive analyticsengine 108 a and a polarity analysis engine, 108 b. In one embodiment,the interface 110 may be configured to present graphical representationsof the PAS's evaluation, analysis, recommendations, and/or polarityanalysis along with user interface features, such as, for examplefeatures that allow the user to submit data to the system, select targetobjectives, make customizations to the analysis, and so forth.

In some embodiments, the operator or user may be a solitary person, agroup of people, a company, or combination thereof. Moreover, it isrecognized that the PAS may be utilized by a variety of types users,which may include, for example, business owners, teachers, governmentworkers, non-profit organizations, and so forth. As such, the PAS may beconfigured to review data from any subject matter and/or from a widerange of organizations including, but not limited to, gymnasiums, yogastudios, manufacturing plants, schools, restaurants, websites, or anyphysical or virtual organization.

III. Exemplary High-Level Data Flow

FIG. 1B is one embodiment of an exemplary data flow using the computingenvironment of FIG. 1A.

In some embodiments, a user utilizes a third party data collectionsystem 106, such as an online survey system, to solicit electronicresponse data, or feedback, from its customers 102, via an online survey(A). The user's customers provide feedback data 102, such as byanswering the survey questions (B). The feedback data 102 may includeboth structured and unstructured data. Further, the feedback data 102can be stored in a database 107 of the third party data collectionsystem (B) which is in communication with the PAS 108 (for example, asurvey results database of an online survey system) or provided directlyto the user device 104. The user then instructs the PAS 108 to uploadthe feedback data 102 and selects a specific objective or goal for thePAS 108 to analyze with respect to the feedback data 102 (C). The PAS108 imports the feedback data from the database 107 (and/or the userdevice 104) into the user's account for processing and analysis (D). ThePAS 108 may conduct a single import, or may conduct periodic batchimports to obtain new updates to the user's feedback data 102.

The PAS 108 then utilizes the predictive analytics engine 108 a toreview the structured data within the feedback data 102 in view of theobjective to identify statistically significant factors that are mostpredictive for the objective and generate a set of correlated structureddata (E). The predictive analytics engine 108 a may generate a graphicalrepresentation of the key predictive factors (or combinations offactors) for presentation to the user via the interface 110 (F). It isrecognized that a variety of predictive analytics engines 108 a may beused and that different predictive analytics engines 108 a may be bettersuited for different types of data, different sized data sets, differenttime processing constraints, and/or different precision requirements. Insome embodiments, the predictive analytics engine 108 a is theCompellon, Inc. predictive analytics engine. While it is helpful to theuser to know which factors are most predictive, correlated structureddata does not provide information as to whether the information ispredictive in a positive manner or a negative manner. Furthermore,correlated structured data does not provide the user with ways to makeimprovements to better meet the selected objective. In addition, thecorrelated structured data does not take into account the free text datawithin the feedback data 102, even though the free text data may includevaluable information for the user.

To address these shortcomings of the correlated structured data, in someembodiments, the PAS 108 utilizes the polarity analysis engine 108 b toanalyze the free text data in view of the correlated structured data andconduct a polarity analysis to identify commonly used terms within thefree text data that correspond to a positive, neutral, or negativesentiment related to the key predictive drivers within the correlatedstructured data (G). The polarity analysis engine 108 b may generate agraphical representation of these terms and their polarity forpresentation to the user via the interface 100 (H). For example, thepolarity analysis engine 108 b may generate a word cloud that is colorcoded to distinguish between positive and negative sentiment and wherethe relative font size of the words indicates the polarity strength ofthe word. The polarity analysis engine 108 b may also use the polarityanalysis to generate recommended actions for achieving the objectivedesignated by the operator (I) and then generate a graphicalrepresentation of the recommendations for presentation to the user viathe interface 110 (J).

As noted above, the feedback data 102 may be in the form of structureddata or unstructured data. Structured data includes to organized data(for example, labeled data), and unstructured data includes data that isnot organized in a pre-defined manner (for example, unlabeled data) ordoes not have a pre-defined data model. Typically, unstructured dataincludes a lot of textual information, but the data may also includenumbers, dates, and other types of information. Free text data, orfree-form text data, is a type of unstructured data that only includestextual information.

The exemplary interface 110 is accessible by the user device 104 andconfigured to receive input and instructions from the user and totransmit that information to the PAS 108. The interface 110 is furtherconfigured to allow the user to customize the analyses that will beperformed by the PAS, such as, for example, selecting the feedback data102, selecting the target objective, selecting a preferred increase ordecrease in the score related to the selected objective, and so forth.The interface 110 is also configured to present the results generated bythe PAS 108, such as, for example, the identified predictive factors,the word cloud, and the recommendations.

IV. Example Use Cases

To illustrate one example use case, the PAS may be utilized by an ownerof a work out gymnasium (referred to as a gym) who wants to improve hisbusiness by determining whether the gym's clients perceive the value oftheir monthly fees to be a worthwhile expense. The gym owner may use thePAS system to determine what features of the business are mostsignificant to his goal based on insight from current clients that viewthe gym in a positive light and those that view the gym in a negativelight. The owner could solicit data from its clients to try and capturefeedback, such as, data about the clients (for example, age, health,education, years at the gym, marital status, or anything else relevant),data about how the clients like or dislike parts of the gym and by howmuch, The owner may utilize a survey that includes field-based questions(for example, age, gender, city of residence), ranking questions (forexample, on a scale of 1 to 5 with 5 being the highest, how clean is thelocker room area), as well as free text input field where clients canprovide their own input (for example, provide any feedback on the lockerrooms). The owner may also utilize data from the business (for example,data from key cards that logs of when clients come to the gym, how longthey stay, what areas they spend the most or least amount of time in,how often they come, the amount they pay for their monthly fee, how longthey have been a member, and so forth). The PAS may then conductanalytics on the solicited data to determine which factors drive in viewof the owner's goal of having clients feel that they are not overpayingfor monthly fees. The PAS may also analyze free text feedback in view ofthe determined factors to identify terms that are used a statisticallysignificant number of times and indicate a significant positive ornegative sentiment. The PAS may then provide a graphical review of thepolarity analysis of the terms so that the owner can see what wordsreflected the most positive sentiment (for example, remodeled, shower,or equipment) or negative sentiment (for example, towels, cheap,expense, or classes). The PAS may utilize the polarity analysis togenerate recommended changes the owner can make (for example, upgrade tobetter quality towels, reduce the price of the “additional fee” classes)in order to achieve the target goal of having the current gym clientsperceive that they are not overpaying on monthly fees.

As another example use case, the PAS may be utilized by a humanresources (HR) manager for a subscription-based social media websitethat is experiencing a lot of employee attrition. The company couldsolicit data from its employees to determine whether the employeesexpect to be at the company in 6 months. The feedback may includefield-based questions (for example, department, years at the company),ranking questions (for example, on a scale of 1 to 5 with 5 being thehighest, how do you rate your manager, how do you rate the free snacks),as well as free text input field where customers can provide their input(for example, provide any feedback on your work area set up). The PASmay then conduct analytics on the solicited data to determine whichfactors drive in view of the HR manager's goal of increasing employeeretention. The PAS may also analyze free text feedback in view of thedetermined factors to identify terms that are used a statisticallysignificant number of times and indicate a significant positive ornegative sentiment. The PAS may then provide a graphical review of thepolarity analysis of the terms so that the owner can see what wordsreflected the most positive sentiment (for example, new, desks, team,approach) or negative sentiment (for example, slow, computers,unrealistic, deadlines). The PAS may utilize the polarity analysis togenerate recommended changes the HR manager can make (for example,institute additional training for managers to help them better setdeadlines for project milestones, upgrading to faster computers) inorder to achieve the target goal of retaining the current employees. TheHR manager can then utilize the PAS to extract the key data points thatare affecting her objective without having to be an expert in datascience or predictive modeling.

V. Computing System Components

A more detailed description of the embodiments of FIG. 1A is nowdiscussed. As noted above, the system architecture 100 shown in FIG. 1Aincludes a user device 104, feedback data 102, a third party datacollection system 106 with a database 107, a PAS 108, and an interface110. In one embodiment, the computing system component such as the PAS108, the user devices 104, and/or the third party data collection system106 electronically communicate via one or more networks, which mayinclude one or more of a local area network, a wide area network, theInternet, or a cloud-computing network, implemented via a wired,wireless, or combination of wired and wireless communication links.

A. User Device

In some embodiments, a user may interface with the PAS 108 using a userdevice 104, which may include, for example, a computer, a laptop, asmart phone, a tablet, a smart watch, a car console, or any othercomputing device. The user may provide feedback data 102 to the PAS ormay instruct the PAS to upload the feedback data 102 from the thirdparty data collection system 106. The user may also provide instructionsto the PAS 108 related to the analyses, such as, for example, dataformats, selection of objective(s), features to exclude from analysis,and so forth. Although FIG. 1A includes one user device 104, it isrecognized as a plurality of user devices (of the same or differenttypes), may be used to interface with the PAS 108. For example, theremay be three different company representatives that each utilize theirown set of user devices to interface with the PAS 108. In addition,there may be a multitude of other clients, each with their own set ofrepresentatives, that utilize user devices to communicate with the PAS108.

B. Feedback Data

In some embodiments, the feedback data 102 includes data solicited froma user's customers, employees, other groups of people of interest to theuser, that provides insight as to a particular topic of interest to theuser, such as, for example, the user's products, services, company,employees, website, and so forth. The feedback data 102 may include datacollected by a third party service system, such as a survey system thatcollects feedback data 102 via online surveys, and may also, or instead,include other data that the user collects through other channels (forexample, paper surveys, employee tracking, product reports, applicationinformation, complaint forms, phone or in-person interviews, and soforth). The feedback data 102 may be stored in a variety of formats andmay include both structured and unstructured data.

The feedback data 102 may be provided to the user device 104, the thirdparty data collection system 106, and/or the PAS 108 from the people ofinterest to the user, where such people submit the feedback data througha device, or a customer, through its device (for example, a computer, alaptop, a smart phone, a tablet, a smart watch, a car console, or anyother computing device).

C. Interface

In some embodiments, the PAS 108 provides results and/or variousinformation to the user's device 104 through an interface system 110.This interface system 110 also allows the user through the user device104 to provide data and instructions to the PAS 108, such as, forexample, select datasets for review or uploading, select objectives,format the data, customize the application, and/or change settings ofthe PAS 108. Thus, the interface 110 may display data, reports,graphical charts, word clouds, user interface tools, recommendations,and so forth.

In some embodiments, the interface 110 is provided as a browser-basedinterface that is accessed by the user device 104 via a standardInternet browser. The PAS 108 includes an interface module configured togenerate instructions for displaying an interface within the Internetbrowser of the user device 104. In other embodiments, the interface 110is provided as a downloadable application that can be run in part or infull on the user device 104.

D. Third Party Data Collection System And Database

In some embodiments, the third party data collection system 106 isconfigured to interface with a set of devices to collect feedback data102. For example, the third party data collection system 106 may be asurvey platform that sends out to a set of recipients, emails or textmessages that include a link to an online survey. The recipients maythen utilize their devices to select the link and access an onlinesurvey of the third party data collection system 106 and to provideresponses to the survey questions. The responses of the recipients maybe stored in the database 107. The database 107 may be implemented asone or more databases of the third party data collection system 106 andmay be stored locally in the third party data collection system 106 orin a remote location. The database 107 may be configured toautomatically store data from the recipients. In some embodiments, thethird party data collection system 106 communicates directly with thePAS 108 to allow the PAS 108 to upload data from the database 107.However, in other embodiments, the third party data collection system106 may allow the user device 104 access to the feedback data 102 suchthat the user device 104 provides the feedback data 102 to the PAS 108.

In some embodiments, the database 107 includes one or more internaland/or external databases, data sources, physical data stores, ornon-transitory memory devices and may be implemented using one or moreof a relational database, such as HBase, Sybase, Oracle, CodeBase, andMicrosoft® SQL Server, as well as other types of databases such as, forexample, a flat file database, an entity-relationship database, anobject-oriented database, or a record-based database.

E. Prescriptive Analytics System

In some embodiments, the PAS 108 accesses feedback data 102 related to auser and stores the data in one or more databases in or in communicationwith the PAS 108. As noted above, the data may come from the user device104, the third party data collection system 106, or another system.

In some embodiments, the PAS 108 includes a predictive analytics engine108 a configured to correlate structured data within the feedback data102 with the objectives selected by the user. The predictive analyticsengine 108 a may then determine which factors are most statisticallypredictive of the target objective and generate a predictive model whichcan be used to predict the likelihood of meeting the target objectivebased on a set of new data. The predictive analytics engine 108 a mayalso include instructions for generating a user interface for presentingthe predictive factors to the user for review.

In some embodiments, the PAS 108 also includes a polarity analysisengine 108 b configured to analyze the correlated structured data inview of unstructured data within the feedback data 102 to identify wordswithin the unstructured data that are commonly used and reflect a strongpositive or strong negative sentiment. The polarity analysis engine 108b may also include instructions for generating a user interface forpresenting a graphical representation of the identified words, such asin a word cloud. The polarity analysis engine 108 b may also use thepolarity analysis to generate recommended actions for the user to taketo address the concern within some of the negative sentiment and includeinstructions for generating a user interface for presenting therecommendations in the interface 110.

VI. Prescriptive Analytics System (“PAS”)

FIG. 2 is a block diagram showing one embodiment of a PAS 210 and itsinteraction with a set of input data 201 and generation of output 230.The PAS 210 is one embodiment of the PAS 108 of FIGS. 1A and 1B.

1. Input

The exemplary PAS 210 includes a data input module 211 within acommunications module 212 configured to electronically communicate withone or more third party systems to receive input data 201, inputsincluding, for example, user-selected objective(s) 202, user collecteddata 204, and feedback data 205.

The objectives 202 may include electronic indications of the user'sgoals or targets the user would like to achieve. Some non-limitingexamples include retaining existing customers, increasing the likelihoodthat customers may recommend the user's product to others, increasingthe likelihood that customers may purchase another product from theuser, increasing the likelihood that customers may spend a certaindollar amount in the next purchase, reducing the likelihood that anemployee may leave the company, increasing overall employee satisfactionwith their job. The input may also include an indication of an amount bywhich the user wants to change a score that indicates an amount or levelat which the user is currently meeting the requested objective. Forexample, only 33% of the responders may indicate that they weresatisfied with the dessert menu options offered by a restaurant. Therestaurant may select an objective of increasing the percentage by 20%such that at least 53% of its customers are satisfied with the dessertmenu options offered by the restaurant.

The user collected data 204 may include information that the usercollects and uploads direct to the PAS 210. For example, separate fromthe third party data collection service, the user may collect data frompaper surveys, customer tracking, application information, complaintforms, phone or in-person interviews, and so forth.

The feedback data 205 may include response data generated by recipientsof the user's survey, such as the user's customers, potential customers,or employees. This data may include structured data based on a finiteset of answers 206 (for example, ranking a provided from 1 to 10 with 1being the least accurate and 10 being the most accurate, True or Falsequestions, providing a score from A to F, and so forth). Feedback data205 may also include unstructured data 207, part of which may be whererecipients provide feedback in free text form 208.

2. Output

The exemplary PAS 210 includes a data output module 213 within thecommunications module 212 configured to generate data to be provided toone or more third party systems. The data output module 213 may beconfigured to generate electronic reports, user interfaces, webpages,instructions for generating webpages, data collections, and so forththat reflect one or more of the analyses performed by the PAS 210.Moreover, the data output module 213 may be configured to instruct auser interface module to present the data to the user or to generatedata packets that include instructions for a remote application topresent the data to the users. The data packets may be encrypted and/orconfigured for delivery over a network for display on a remote device.The exemplary outputs 230 include key predictive factor(s) 238, polarity236, word clouds 232, and/or recommendation(s) 234.

The key predictive factors 238 may include a listing or depiction of thefactors the PAS (or the predictive analytics engine) has identified ashaving a strong influence on the target objective. The key predictivefactors 238 may include single factors such as age, household income,number of nights per month spent in a hotel, and so forth. The keypredictive factors 238 may also include combined factors, such as thecomfort of the hotel room combined with the quality service at check in.

The recommendations 234 may include changes generated and selected bythe PAS 210 based on the user's objective(s) 202 or focused objectivedelta, polarity analysis, and the correlated structured data generatedby the prediction model. The focused objective delta may include anamount, such as a percentage, that the initial target objective ischanged. The focused objective delta may be selected by the user and/orrecommended by the PAS 210. The recommendations 234 may be different fordifferent objectives 202 even if the applied prediction model is basedon the same data (may include part or all of user provided data 204and/or part or all of feedback data 205). The focused objective deltamay relate to an overall objective (for example, increase the likelihoodthat a gym client will recommend the gym), or may relate to changes thatcan be made to influence responders that provide negative feedback (forexample, address the smelly towels highlighted by the detractors) aswell as responders that provide positive feedback (for example, increasethe number of free classes for long standing members).

The polarity 236 may include an indicator of the sentiment combined withthe predictive importance of specific words, phrases, or specificfactors in view of the selected objective. The key predictive factors238 may be represented as a list or graphic showing which factors arethe most to least predictive based on the objective(s) 202. The factors238 may include features, services, products, or aspects of a particularuser's business or topic, or the like.

The word cloud 232 may include a graphical word structure thatidentifies commonly used words that drive positive or negative sentimentwhich either supports or hinders the objectives. To achieve theobjective(s) 202, the user can increase/improve the identifiedsupporting factors by a certain percentage or decrease the identifiedhindering factors by a certain percentage, according to the predictionmodel. As one example, the PAS 210 may generate a custom word cloudimage based on a specific recommendation 234. The custom image maycontain words that have been provided to the system by the user or itscustomers. Additionally, the words may be represented by some, all, orany combination of the following, but also not limited by, frequency,connotation, magnitude, sentiment, relevance to the recommendation,order received, or date received. One example for showing sentimentwould be for the PAS to use different colors for positively andnegatively correlated terms, where positive terms are one color andnegative terms another color. One example for showing relevance to therecommendation could be for the most relevant terms to appear in thecenter of the image and less relevant terms to appear on the edges.Another example for showing relevance to the recommendation could be toshow larger words for the more relevant terms and smaller words for theless relevant terms, or a combination of size and location. The wordcloud 232 may be representative of one recommendation 234, a group ofrecommendations, or an overall assessment of the objective 202.

3. PAS Components

The exemplary PAS 210 includes a communications module 212, a datamodule 218, a predictive analytics engine 228, and a polarity analysisengine 214.

a. Communications Module

The exemplary communications module 212 is configured to facilitatecommunication between the PAS 210 and other systems or devices. Thecommunications module 212 may include the data input module 211 and/orthe data output module 213. The data input module 211 is configured toreceive and process various input data 201 into the PAS 210. The dataoutput module 213 is configured to process and format various data andresults of the various analyses for access by other systems, such as theinterface or third party systems.

b. Data Module

The exemplary data module 218 is configured to control and manage thestorage of data within the PAS 210 as well as responding to requests foraccessing or updating the data stored within the PAS 210.

c. Predictive Analytics Engine

The exemplary predictive analytics engine 228 is configured to analyzestructured data in view of a selected objective to identify factors orgroups of factors that have the most predictive effect on the objectiveand generate a corresponding predictive model. The predictive analyticsengine 228 may also be configured to regenerate or update the predictivemodel based on new or updated feedback data. The predictive analyticsengine 228 is one embodiment of the predictive analytics engine 108 a.

d. Polarity Analysis Engine

The exemplary polarity analysis engine 214 is configured to analyzeunstructured data in view of the correlated structure data and theselected objective to determine polarity of key terms within theunstructured data. The polarity analysis engine 214 may include, but isnot limited to, a free text analyzer module 215, a scoring module 216, adata module 218, a polarity module 220, a free text filter module 222, adata feedback module 224, evaluation module 226, and a prescriptiveanalytics module 228. The polarity analysis engine 214 is one embodimentof the polarity analysis engine 108 b.

The exemplary free text analyzer module 215 is configured to analyzedata that is categorized as free text data and may, for example, groupwords or terms together based on their lemma, which results in groupingwords together that are different but have the same base form. Forexample, the free text analyzer module 215 may group the words “talk,”“talks,” and “talked” together as a single base word category and assignit the lemma “talk”.

The exemplary free text filter module 222 is configured to apply anelectronic filter to the categorized free text data to identify specificwords of interest, such as actionable words like nouns and adjectives,in view of the user objective(s). The free text filter module 222 isalso configured to correlate the filtered and categorized text data withthe structured data.

The exemplary scoring module 216 is configured to analyze the user'sobjectives against the structured data within the feedback data togenerate a score indicator of how well (if at all) the user is meetingthe objective and/or a set of correlated structured data. The scoringmodule 216 may also be configured to analyze the correlated structuredata in conjunction with the filtered, lemmas to create an aggregate oflemmas scored by the objective.

The exemplary data feedback module 224 is configured to analyze thedistribution of the filtered lemmas against the distribution of theobjective(s) in the original, correlated structured data to provide thecontext of polarity. The data feedback module 224 is configured toanalyze the resulting feedback based on how often or not the lemmaappears, for example based on rarity and/or aggregate frequency.

The exemplary evaluation module 226 is configured to create finalpolarities based on the newly distributed lemmas with independenttreatment of positive and negative terms. In one embodiment, theevaluation module 226 assigns a polarity strength value between −1 to +1to the lemmas where −1 represents negative sentiment and 1 representspositive sentiment.

The exemplary polarity module 220 is configured to store the assignedpolarities of each, but not necessarily every, lemma. In someembodiments, the polarity module 200 is configured to creating graphicalrepresentations that depict the predictive terms, their correspondingfrequency, and polarity in an image or display, such as, for example, aword cloud.

It is recognized that there are other embodiments of the polarityanalysis engine which may exclude features of the exemplary polarityanalysis engine 214 and/or may include additional features. As such,some of the processes and/or modules discussed herein may be combined,separated into sub-parts, and/or rearranged to run in a different orderand/or in parallel. In addition, in some embodiments, different blocksmay execute on various components of the PAS 210.

VII. Prescriptive Analytics System Processes

FIGS. 3, 4, 5, 6, and 7 are flowcharts illustrating various embodimentsof Prescriptive Analytics System processes that execute within thecomputing environment. In some embodiments, the processes are performedby the PAS 210, as show in FIG. 2 , and/or by one of its components.However, it is recognized that other components of the PAS or othercomponents (not shown) may perform one or more of the processes. Forease of explanation, the following describes the services as performedby the PAS 210. The example scenarios are intended to illustrate, butnot to limit, various aspects of the computing environment. In someembodiments, the processes can vary from the exemplary flowcharts, withsome blocks omitted and other added.

A. Polarity Analysis

FIG. 3 is one embodiment of a block diagram which illustrates a processfor conducting a polarity analysis which may utilize various componentsof the PAS 210 including, for example, communications module, predictiveanalytics engine, and polarity analysis engine, according to oneembodiment.

Beginning at block 304, the user sends a survey to a target group andcollects a set of results from the responders and utilizes the PAScommunications module 212, as shown in FIG. 2 , to submit the set ofresults to the PAS and/or instruct the PAS to upload the results. Theresults may include both structured and unstructured data, such as freetext data. In block 306, the user utilizes the communications module tosubmit selected or created objectives to the PAS as well as otherinstructions, such as data to exclude or include in the analysis, dataformatting instructions, and so forth.

In block 308, the PAS receives the results data and objective(s) fromthe user through the communications module. In block 310, the PredictiveAnalytics Engine of the PAS processes the structured data in view of theobjective(s) to identify key predictive factors, generate a predictivemodel, and generate correlated structure data.

In block 320, the Polarity Analysis Engine of the PAS 210 accesses theunstructured data, or free text data, of the results, processes the datausing natural language processing, which may include, tokenizing,sentence splitting, part of speech analysis, or converting terms intotheir lemma standard form. In block 322, the Polarity Analysis Engineprocesses the free text data to convert the terms in the data into theirstandard lemma form. In block 324, the Polarity Analysis Engine thenfilters the set of standard lemma terms to flag the actionable words,such as the nouns and adjectives. Other words, such a prepositions,pronouns, and articles are flagged as nonactionable and/or removed fromthe working set of free text data. In some embodiments, the PAS mayreceive the focused objective delta from the user or optionallyautomatically generate a focused objective delta to apply in block 326.The focused objective delta may include a percentage by which the userwishes (or the PAS recommends) to influence or change the initial targetobjective. Blocks 322 or block 324, or both, may include a machinelearning algorithm either by itself or in combination with otherprocessing or filtering methods. In block 326, the Polarity AnalysisEngine aggregates the filtered set of standard lemma terms with thecorrelated structured data based on the focused objective delta.

In block 328, the Polarity Analysis Engine scores the standard lemmaterms in view of its corresponding correlated structured data and theuser's objectives. The scoring may be based on a machine learningalgorithm that generates a word structure. In block 330, the PolarityAnalysis Engine conducts a feedback analysis to analyze the distributionof the lemma terms against the distribution of the objective in thecorrelated structured data as well as the rarity and/or aggregatefrequency of the terms. In block 332, the Polarity Analysis Enginegenerates polarities based on the distributed lemma terms. In someembodiments, the polarities may be assigned on a strength scale of −1.0to +1.0 with −1.0 indicating most negative and the +1.0 indicating mostpositive. The PAS may store the polarities of some, but not necessarilyevery, term, and the polarities can be used in the creation of graphicalrepresentations of words and their corresponding predictive nature andpolarity (for example, a word cloud).

In block 334, the Polarity Analysis Engine analyzes the polarities, theobjective(s), and the standard correlated data to generaterecommendations to present to the user that the user could take tobetter meet the objectives.

It is recognized that the PAS may provide some or all of the datagenerated to the user for review and/or to see possible input from theuser. In one embodiment, the relevant information may be output via theCommunications Module of the PAS. For example, the final polarities andcomputations used to calculate the final polarities may be sent to adevice or system utilized by the user to view and for the user to makeany desired changes. Also, it is recognized that a variety ofembodiments may be used to conduct the analyses discussed above and thatsome of the blocks above may be combined, separated into sub-blocks, andrearranged to run in a different order and/or in parallel. In addition,in some embodiments, different blocks may execute on various componentsof the PAS or various connected systems.

B. Recommendation Generation

FIG. 4 schematically illustrates a logical flow diagram for oneembodiment of an example process for generating and providing a list ofrecommendations to achieve a preferred amount to change an objective.

In block 402, the PAS creates an account for a user. This may be doneautomatically with or without a user's interaction, or programmed by asystem administrator. This account will allow the user to customize itsown user interface to be used with its own data and analyses.

In block 404, the PAS accesses, receives, or retrieves data, includingdefined objectives and user data. This information may includeobjective(s), general data, or customer provided data. The data may beformatted as unstructured data, including free text data, or structureddata. The PAS can optionally be configured to retrieve information fromanother database, or the PAS can be configured to receive informationdirectly uploaded by a user.

In block 406, the PAS prepares the data for processing, which mayinclude automatically classifying data types (for example, ordinal,numerical, date, name, and so forth), positions, names, or decidingwhether to exclude the data variable. The PAS may allow the user toverify and/or edit the automated classifications.

In block 408, the PAS analyzes the input data such that each variable,and combinations thereof, in the data set are compared to othervariables, and combinations thereof, to assess the predictive strengthof each variable or set of variables tied in view of the defined userobjective(s), as received by the PAS, to generate a prediction model.

The PAS then may then perform one or more of three processes which canoccur in any order, or not at all. The PAS may, (1) provide to the userthe analysis including relevant, redundant, related, or predictiveness,(2) allow the user to select variable(s) to influence and providerecommendations for achieving the desired results, or (3) analyzeadditional data to (a) test the previously generated prediction model,(b) retrain the previously generated prediction model, or (c) predict anoutcome with simulated data.

For providing the user the analysis, which is not shown in FIG. 4 , thePAS may provide results of analysis to the user, which can includeinformation on which parts of the data are relevant, redundant, orrelated, as well as which data variables, or combinations of variables,are the most to least predictive based on one or more complexstatistical analyses. A variety of analyses may be used.

For analyzing additional data, which is not shown in FIG. 4 , the PASreceives or accesses additional data that was not in the original dataset to either, (a) test the previously generated prediction model toensure performance, (b) to retrain the predictor by appending new datato original dataset, or (c) analyze and produce predictions forsimulated data including either single data point entry or bulk dataentry.

For allowing a user to select a preferred amount to change theobjective, block 410, the PAS may allow the user to select a preferredamount to influence the objective, by either increasing or decreasingthe value, to provide an indication of the amount other variables orscores for preferred outcomes should be adjusted. For example, toincrease car sales by a certain percentage, increasing the number of carsalesman or providing additional training to the car salesmen, orproviding bilingual car salesmen, may be a recommended action that maybe taken by the user to achieve the desired increase in car sales. Thisis referred to as the “focused objective delta.” In some embodiments,the delta is selected by the user via a slider bar that indicates apercentage of change, but other user interface features could be used.Further, the delta may be represented in other ways, such as, forexample, as a request to “increase” a positive factor or “decrease” anegative factor without providing a specific amount by which the deltashould be. The PAS may also automatically create this focused objectivedelta and/or may enable a user to edit it. The PAS may also allow theuser to select variables to exclude from the analysis, and the PAS mayautomatically exclude variables on its own. For example, because the ageof customers cannot be changed by the user, the user may opt to excludeit from the analysis, thereby preventing any generated recommendedactions from appearing that recommend the user change the customers'ages to be younger or older or within a specific range.

In block 412, the PAS analyzes associated values and inputs to determinerecommendations based on selected criteria and non-excluded variables.So, after the PAS receives the input of what the desired change is, thePAS may analyze associated values and inputs to determinerecommendations. The PAS may provide the automatically generatedrecommendations for achieving the desired changes to the user. Anexample embodiment of block 412 can be found in FIGS. 5 and 6 below.

In block 414, the recommendations from block 412 are formatted to beprovided to the user. The PAS may optionally provide to the user asubset or group of data values for each recommendation analyzed in apresentation (for example, a word cloud) that visually represents theimportance of each word or term, possibly represented by size, and anadditional sentiment element, possibly represented by color.

It is recognized that a variety of embodiments may be used to conductthe analyses and that some of the processes above may be combined,separated into sub-blocks, and rearranged to run in a different orderand/or in parallel. In addition, in some embodiments, different blocksmay execute on various components of the PAS.

C. Free Text Data Processing

FIG. 5 schematically illustrates a logical flow diagram for oneembodiment of an example process for preparing specific data forprocessing. FIG. 5 is a more detailed explanation of one of a possiblemultitude of processes of block 412 in FIG. 4 , such that after the PASallows the user to select a preferred amount to increase or decrease theobjective, and may also allow the user to select predictive factors toexclude from analysis in block 410 of FIG. 4 , FIG. 5 encompasses one ofthe events or processes of block 412.

In block 504, the PAS accesses the free text data. The unprocessed freetext data is converted, in block 506, by the PAS into a lemma standardform using one or more industry standard approaches. The processing mayalso include one or more of, tokenizing, sentence splitting, part ofspeech analysis, or lemma analysis on the free text.

In block 508, the PAS filters text data for actionable words, such asnouns and/or adjectives. This filtration process flags key predictiveterms and words to be used in further analysis with the remainingnon-free text data.

In block 510, the PAS aggregates the filtered text data with thestructured data previously included in the analysis by the user.

It is recognized that a variety of embodiments may be used to conductthe analyses and that some of the processes above may be combined,separated into sub-blocks, and rearranged to run in a different orderand/or in parallel. In addition, in some embodiments, different blocksmay execute on various components of the PAS. In other embodiments, anyof blocks 504, 506, 508, and 510 can be implemented in any appropriateor logical order during processing, and/or in tandem. Also, in otherembodiments, the PAS may choose to use different methods of processingthan those listed here, as well as only using one, none, or more methodsthan those that appear in FIG. 5 .

D. Processing and Analysis of Data

FIG. 6 schematically illustrates a logical flow diagram for oneembodiment of an example process for analyzing data and generatingrecommendations based on polarity. FIG. 6 is an additional embodiment ofblock 412 in FIG. 4 such that after the PAS processes the data in FIG. 5, FIG. 6 encompasses one of the events or processes after or during theevents of FIG. 5 .

In block 602, after data has been processed, the PAS takes the focuseduser objective(s) data and applies it against the structured data andcorrelated free text data, if any, and creates an aggregate of lemmasscored by the focused objective. The focused objective data may includethe percentage by which the user wishes to influence the initial targetobjective. The PAS uses the aggregate of lemmas to indicate how stronglythey affect or influence the objective.

In block 604, the PAS analyzes the distribution of lemmas against thedistribution of the focused objective(s) in the original structured datato assign polarity to the lemmas.

In block 606, the PAS uses a feedback procedure to balance the dataagainst rarity and aggregate frequency of the data.

In block 608, the PAS creates final polarities based on the newlydistributed lemmas, possibly with independent treatment of positive andnegative terms. Also, the treatment of positive and negative terms maybe set to a strength scale, such as −1 to 1.

In block 610, the PAS generates recommendations based on the inputs andanalysis procedures. These recommendations may be used as part, of theentire, basis of the PAS' conclusions, predictions, and recommendationsto the user.

It is recognized that a variety of embodiments may be used to conductthe analyses and that some of the processes above may be combined,separated into sub-blocks, and rearranged to run in a different orderand/or in parallel. In addition, in some embodiments, different blocksmay execute on various components of the PAS.

E. Processing and Analysis of Data

FIG. 7 is a block diagram showing one embodiment of an example processfor analyzing free text data and key predictive factors to generate agraphical representation of words based on their predictive nature andpolarities. The PAS may apply its architecture and processes for theautomated processing of large data sets with identified key predictivefactors to conduct a polarity analysis on the data sets forautomatically identifying recommended actions for improvement, and topresent the recommended actions in a graphical word structure thatidentifies words that drive positive or negative sentiment and supportsthe recommended actions (for example, word cloud).

The PAS uses the key predictive factors 702, focused objective delta703, and free text data 704 as inputs. The key predictive factors 702are produced by the PAS analysis as described herein. The focusedobjective delta 703 may include the percentage by which the user wishesto influence the initial target objective, and may be set by the user oris set automatically and may be updated by the user. The free text data704 is processed and analyzed as described herein. The key predictivefactors 702, the focused objective delta 703, and free text data 704 areused to create a word selection based on final polarities 706. Then, thePAS generates a graphical representation in block 708. Lastly, the PASmay provide a graphical representation, which may be a word cloud, tothe user in block 710.

F. Other Processes

It is recognized that the PAS may include other processes not describedabove or included in the Figures. For example, in some embodiments, thePAS is further configured to run a responder score process forgenerating a responder score (sometimes referred to as a net promoterscore) for the analyzed data (for example, user collected data 204and/or feedback data 205) based on the user objective(s) 202. Thisprocess divides the responders into three categories: promoters,passives, and detractors. Promoters include responders who areenthusiastic towards either the company or the defined topic. Passivesare responders who are indifferent, or neither enthusiastic nor unhappy,towards the particular topic. Detractors are responders who are unhappytowards the particular topic. The process may calculate the responderscore by subtracting the percent of detractors from the percent ofpromoters, where the Responder Score=% Promoters−% Detractors.

This responder score may be used by the PAS to identify preferredtarget(s) to market to, or focus on. For example, a user, or businessowner, may choose to focus on promoters so that the business owner canmonitor the parts of the business that are most liked and improve theexperience even more, or increase awareness of those parts. As anotherexample, the PAS may use the responder score to identify preferredtarget(s) to or focus on as a way to minimize factors that contributedto negative sentiment. For example, a business owner may choose to focuson detractors so that the business owner can address the parts of thebusiness that are most disliked to reduce the negative experience orimpact on the responders. Either example may be used to improve theresponders' experiences, at least in part or on average. It isrecognized that both examples can be used separately or in tandem.User(s) may choose promoter analyses, detractor analysis, or acombination of the two, based on any number of factors. For example,some factors may include the ability to change the recommended parts, auser's cost-sensitivity, or the time it may take to implement anychange(s).

VIII. User Interfaces

In some embodiments, the computing environment, including the PAS,includes modules for providing graphical user interfaces that allow auser to interact with the PAS via a user device. The modules mayinclude, but are not limited to, an application on the user's device, acloud-based program, a remote application, or a web-based interface.Example embodiments are described as follows.

FIG. 8 is a screen shot depicting one embodiment of an example userinterface that generates a graphical display of various data setsassociated with a specific account. A user can contribute data to anaccount by selecting the “Upload” button, which allows the user toupload a locally stored file or provide a particular location to a fileon a server. The user then has access to process and analyze theuploaded (or retrieved by the system) data such as “HotelSatisfaction—Net Score.” The interface generates a graphical depictionof information for each data set, which may include, for example, nameof the data set, user specified objective(s) or “Targets”, columns ofdata, rows of data, file size of the data, when the data was uploaded,and when the data was first created. The interface allows a user to sortthe data sets with specified criteria (for example, name, size, date,and so forth). A user can use the search box to search the data sets. Auser can set the viewer to view the data sets in a grid, list, or otherformat by selecting the button with four squares on the top right. Theuser can choose to prepare the data by selecting “Go” to verify theautomated classification provided by the system and to make anynecessary or preferred changes to the classification. After preparation,the button is replaced with a set of buttons, such as “Review,” “Model,”and “Action,” that may appear when a mouse hovers over the section, oris constantly displayed to the user. The “Review” button includessimilar capabilities as the “Go” button used to prepare the data. The“Model” button allows the user to design or view a previously generatedmodel as described below. The “Action” button allows the user to createa custom target as described below. By selecting the three dotsassociated with each uploaded dataset, the user can view additionaloptions to interaction with the dataset. In the present embodiment, theuser may “Rename” the dataset or “Delete” the dataset. The user can alsouse a “Discover” button, which is not shown, to apply an associatedgenerated prediction model. The user can use a “Predict” button, whichis also not shown, to forecast hypothetical results based on the user'spreferences.

FIG. 9 is a screen shot depicting one embodiment of an example userinterface for uploading data or directing the system to a remotelocation where data is located and available for download. This screenis accessed by selecting the “Upload” button in FIG. 8 . The user hasseveral options for providing data to the system, (1) the user can dragand drop a file including data, (2) the user can select the “click hereto choose a file” button to manually select the data file from a localor network storage, or (3) the user can upload data via a URL hyperlinkthat points to a dataset that may be stored remotely, and provide anynecessary authentication for access. The system may support a variety ofdata formats including, for example, comma-separated values (CSV), tabseparated values (TSV), a compressed file format, such as ZIP, thatincludes either CSV or TSV, or both, or other supported formats. Byselecting “Custom Empty Values,” the user can provide values that theprogram considers “empty” for the purposes of the analysis associatedwith the user imported dataset.

FIG. 10 is a screen shot depicting one embodiment of an example userinterface displaying options for applying the automatic dataclassification, and also allowing for additional classification andcustomization by the user as well as the implementation of a schema. Auser may access this screen by selecting “Go” to prepare the data,“Review,” or selecting the dataset name, under the associated dataset asdepicted in FIG. 8 . The user has the ability to view the system'sautomated classification, which includes information such as, but notlimited to, the survey questions made available to the user's customers,the position of each question on the survey relative to the others,whether the system will exclude any particular question in its analysis,any particular issues with parts of the data (for example, “overlyunique,” “possibly ordinal,” “possibly categorical,” and so forth), thedata type (for example, categorical, numeric, ordinal, and so forth),what parts of the ordered or categorical are separated into itsparticular classification, statistics showing graphs and basicstatistical analysis for each individual question, how many customersleft the field empty, and how many unique responses were provided. Theuser may also edit several of these fields, with the additional optionto exclude parts of the dataset (for example, by checking a box toexclude). A user may hover over or select the plus sign near the datasetname to access or view additional information about the dataset (forexample, file size, date created, and so forth). The user may alsoselect the “Options” tab to access several other options, such as, butnot limited to, previewing the data in a row and column matrix,importing a schema, or exporting a schema. A schema includes a presetscheme of classification that the system can use to apply to theexemplary dataset. A schema may help the user apply the same presetconditions to multiple datasets with ease, rather than making changesmanually in each instance. The imported and exported schema files may bein any supported format, such as, for example, CSV or JavaScript ObjectNotation (“JSON”). The user may select “Continue” to view the userinterface in FIG. 11 , which allows the user to design or selecttarget(s) or objective(s). The user may select “Datasets” (not shown) toreturn to the page illustrated in FIG. 8 .

FIG. 11 is a screen shot depicting one embodiment of an example userinterface for inputting pre-set criteria to analyze data with additionalcustomizations. The user may perform a Net Score Analysis for a selectedtarget (for example, “Likelihood to recommend”) based on a particularquestion. The user may, design a name for the target, add exclusions(for example, exclude a particular question from the analysis), select asubset target, define promoters or detractors, edit statisticalvariables (for example, cardinality, confidence level, cost of falsepositives, and/or cost of false negatives), or select whether to includeempty target rows. The user may select “Submit Target” to begin thesystem's processing and analysis to create a prediction model. It isrecognized that a variety of prediction models may be generated by oneof the many modeling techniques. The user may also select “CustomTarget” to view a user interface, similar to FIG. 24 , which allows foradditional options.

FIG. 12 is a screen shot depicting one embodiment of an example userinterface that generates a graphical display of the results of ananalysis. After a user selects a target and programs the desiredcriteria and selects “Submit Target” in FIG. 11 , this page showsresults of the Net Score Analysis. Information made available to theuser may include, but is not limited to, net score, percent ofdetractors, passives, and/or promoters related to the full set of data,number of total survey questions analyzed, number of questions thesystem deems relevant to the analysis, number of questions the systemdeems predictive relevant to the analysis, sample recommendation(s) toimprove net score, detailed list of predictive question with varyingpredictiveness, or total combinations the system analyzed and the amountof time it took the system, including any appropriate or relevantinformation that would be useful to the user. In some embodiments, theanalysis by the PAS 108 involves the use of scoring, or calculating aNet Promoter Score for the data based on the user objective(s). Thissystem of scoring divides a company's customers into three categories,promoters, passives, and detractors. Promoters may include customers whoare enthusiastic towards the either the company or the defined topic.Passives may include customers who are indifferent, or neitherenthusiastic nor unhappy, towards the particular topic. Detractors mayinclude customers who are unhappy towards the particular topic. TheScore is a subtraction of the percent of detractors from the percent ofpromoters, where the Score=% Promoters−% Detractors. The user also hasthe ability to edit, delete, or mark for future reference the exemplarytarget. The user may select “Datasets” (not shown) to return to theinterface illustrated in FIG. 8 . The user may select the dataset'sname, in this example “Hotel Satisfaction—Net Score,” to return to auser interface as depicted in FIG. 10 . A user may hover over or selectthe plus sign near the dataset name to access or view additionalinformation about the dataset (for example, file size, date created, andso forth). The user may select “Promoter” tab to view a user interfaceas depicted in FIG. 14 . The user may select “Detractor” tab to view auser interface as depicted in FIG. 20 .

FIG. 13A is a screen shot depicting one embodiment of an example userinterface for the setting of a goal based on the focused dataelement(s). The user may select an amount the user would like the netscore to change for the selected goal (for example, +5 in the exemplaryexample), or input the user's own desired change to the score usingvarying methods (for example, typing, dragging a slider, or by voice).Thus, the user interface allows the user to not only indicate the typeof change but a desired amount of change. This change may be referred toas the focused objective delta. The user may exclude certain questionsthe system designated as predictive to the analysis. A user may chooseto exclude information that the user cannot affect through any action ofits own, or any affect would be too cost-prohibitive, or out of thescope of the user's abilities. For example, the user may exclude age,gender, or race because those are factors the user cannot change in thecustomer base, but keep in monthly price, staff service level, and classsize because those are factors the user could change. Alternatively, thesystem can run the analysis without the user's active participation byusing default settings. The user may also reset the analysis criteria todefaults. The user may select a button (not shown) to send theinformation to the system, or it can be sent automatically. After theinformation is sent, the system then calculates its analysis andprovides recommendation(s) to the user.

A slider bar may also be used as a form of data input where a user maymove a slider back and forth along a bar representing a desired increaseor decrease of the focused data element(s). The user may move the sliderbar with various forms of input; for example, but not limited to, theuse of a mouse on a computer to click and drag the slider bar, or inanother example, a user may touch a touch-enabled input device, such asa phone or tablet, to move the slider bar.

FIG. 13B is one embodiment of a screen shot showing multiple possiblerecommendations to achieve the selected goal based on the analysisperformed, the user may view one or more recommendation(s) that showways to achieve the desired change in the net score. This example showsmultiple recommendations for the same set criteria in FIG. 13A. Thesystem's recommendation(s) may include information that indicates thepercentage the user may increase or decrease the frequency of types ofanswers received by a specific predictive question to achieve thedesired goal. Color may be used as an indicator for whether to increase(for example, green) or decrease (for example, blue or purple) aspecific answer to a predictive question. These answers may beinfluenced by actions in the physical world such as cleaning thefacilities or implementing more effective training for staff.

FIG. 13C is one embodiment of a screen shot showing a word cloudpertaining to a specific recommendation, where the word cloudgraphically represents the predictive language further categorized bysentiment. In some embodiments of the user interface, there may be aword cloud button that, once selected, generates graphicalrepresentation(s) of words and their corresponding predictive nature andpolarity. Polarity may be depicted with positive and negative sentimentindicated with colors, such as, for example, green and purple,respectively. The words can also be depicted by size, such that largerwords or terms are more predictive, or have a higher influence on thedesired goal, and smaller words or terms are less predictive, or have alower influence on the desired results, pertaining to the specificrecommendation selected. For example, the purple words “parking” or“locker,” assuming both are the same size, indicate negative words thathave the largest negative sentiment. The word cloud may also beautomatically generated.

FIG. 14 is a screen shot of one embodiment of an example user interfacethat generates a graphical display of additional information aboutsurvey promoters. Information may include, but is not limited to,percent of detractors, passives, and/or promoters related to the fullset of data, number of total survey questions analyzed, number ofquestions the system deems relevant to the analysis, number of questionsthe system deems predictive to the analysis, and detailed list ofpredictive question with varying predictiveness. The user may select anynumber of tabs for further analysis or viewing including, but notlimited to, the exemplary tab “Summary,” “Model Structure,” “RelevantQuestions,” “Quality.” Some of these mentioned pages are illustrated infollowing figures. The user may also have the ability to edit, delete,or mark for future reference the exemplary target (not shown). The usermay select the dataset's name, in this example “Hotel Satisfaction—NetScore”, to return to a user interface as depicted in FIG. 10 . A usermay hover over or select the plus sign near the dataset name to accessor view additional information about the dataset (for example, filesize, date created, and so forth).

FIG. 15 is a screen shot depicting one embodiment of an example userinterface that generates a graphical depiction of the system'scalculated path for its prediction model for reaching the designatedtarget goal where certain data elements hold more or less predictivevalue. The user interface also including a graphical representation ofthe strength of the predictors, one example of a way to show this is touse varying shades of color and varying combinations of the dataelements. This user interface may be accessed by selecting “ModelStructure” as it appears in a user interface as depicted by FIG. 14 , orother similar Figures. The user interface shows the user a graphicalrepresentation of each of the analyzed predictive questions, andcombinations thereof, and graphically represents strong predictivenessand weak predictiveness, which may be designated by color or shading.This may be referred to as a model graph, and is a visual representationof how the user's variables combine to identify and predict the settarget. In this embodiment, the color and shading of each variablerepresents the relative importance or strength in predicting the settarget, and the super variables (SV) represent combined driver variablesor questions. The user may also view the performance of the model inidentifying target respondents, where the model expects to perform witha certain percentage and/or number of results that the model arepredicted positive, and are actually positive, predicted negative butare actually positive, predicted positive but are actually negative, andpredicted negative and are actually negative. The user interface mayalso provide statistics on the model which may include, but are notlimited to, accuracy, precision (for example, predicted targetresponse(s)), efficiency, sensitivity (for example, coverage),specificity, rows analyzed, and population percent (for example,exemplary target response(s)). The user may also view specific targetdetails on this user interface page. The user may have the option todownload the graph. The user may also have the option to move the grapharound a particular viewer and/or zoom in and/or out to make the graphappear larger or smaller.

FIG. 16 is a screen shot depicting one embodiment of an example userinterface that generates a graphical display of specific profiles of themost positive and the most negative characteristics of individuals in adata set pertaining to the promoter analysis. This allows the user tosee which types of profiles are most and least useful in achieving theobjective(s). The exemplary screen shot illustrates the “BestIndividuals Combinations,” which may display a list of single points inthe data set that represent the best individual profiles for thecriteria that would be a good target respondent and the relatedreasoning. The table shows the profiles of the individuals who are moreor less likely to be the best for the user to target to achieve itsdesired objective/goal initially set out for the analysis. The interfacemay also display relevant information related to each individual profilelisted, such as, but not limited to, a set number of the top and bottomprofiles for the most and least likely to be a target respondent, theexemplary target rate as calculated by the PAS, the questions the PASdeems most predictive and the answers associated with those questions,individual target rate, which is the predicted target rate for eachindividual profile, and the ratio to the population, which is theindividual target rate divided by the exemplary target rate. The mostand/or least likely individual profile to be a target respondent may bedesignated by color, such as green or purple, respectively. The user mayhave the ability to download the selected profiles as well as excludedata in the downloaded file.

FIG. 17 is a screen shot depicting one embodiment of an example userinterface that generates a graphical display of how data correlates witha positive or negative influence on an objective, such as how a positiveor negative survey answer correlates with the target objective. For thequestions included in the PAS analysis, the answers associated with eachquestion are analyzed and rated as to how predictive the answer tends tobe in relation to the initial criteria the user set. The system can alsoconclude whether the answer is positive or negative. The informationshown may include, but is not limited to, whether the associated answerhas a “positive” or “negative” relationship to the initial objective(s),ranked in a particular order, or the strength of the relationship of theassociated answer to the initial objective(s), ranked in order ofstrength. The relationship may be classified as “strong,” “medium,” or“weak.” The interface may also include a filter for the user to filterthe data by influence or strength, such as that the user may choose toview only the data the system designates as “strong” data, or “positive”data. Colors may be used to illustrate the positive or negativerelationship, such as green or purple, respectively. The most predictivequestions, as calculated by the PAS, may be marked accordingly. The usermay also have access to statistical results and graphs for each of thequestions to view the distribution by selecting a link or button on thepage associating with each question. Data that the user may see mayinclude, but is not limited to, data type, name, total values, emptyvalues, unique values, graphs of the distribution, distribution againstthe target, or a cumulative graph, and values designated as positive ornegative (also as depicted on the related graphs). A user may view theanalysis for one or more questions at a time.

FIG. 18 is a screen shot depicting one embodiment of an example userinterface that generates a graphical display showing how data andcombinations of data correlate with a positive or negative influence onan objective, such as how two or more survey questions have a positiveor negative relationship to the target objective(s). Certain questionpairs may have a significant relationship to the initial target as setby the user. The relationship of each response pair may be classified as“strong,” “medium,” or “weak.” The interface may also include a filterfor the user to filter the data by influence or strength, such as thatthe user may choose to view only the data the system designates as“strong” data, or “positive” data. Colors may be used to illustrate thepositive or negative relationship, such as green or purple,respectively. Answers to particular paired questions that the PAS hasdetermined meets deems a predetermined threshold of a relationship maybe ranked or listed such that the user may view which answers relatepositively or negatively and with what strength.

FIG. 19 is a screen shot depicting one embodiment of an example userinterface that generates a graphical display of the relationship betweendata points and how they lead to an objective pertaining to the promoteranalysis. This user interface may be accessed by selecting “RelevantQuestions” as it appears in a user interface as depicted by FIG. 14 , orother similar figures. The graph visually depicts, visualizes therelated questions, their direction, and strength of influence to theinitial target as set by the user. Color and/or shading may be used toindicate the strength of the relationship to the final target, where,for example, the brighter color is more predictive. The user may alsodownload the graph, move the graph around a particular viewer, or zoomin or out to make the graph appear larger or smaller.

FIG. 20 is a screen shot depicting one embodiment of an example userinterface that generates a graphical display of the results of ananalysis with respect to detractors related to criteria set. Theanalysis may include, but is not limited to, percent of detractors,passives, and/or promoters related to the full set of data, number oftotal survey questions analyzed, number of questions the system deemsrelevant to the analysis, number of questions the system deemspredictive to the analysis, or detailed list of predictive question withvarying predictiveness. The user may select any number of tabs forfurther analysis or viewing including, but not limited to, the exemplarytabs “Summary,” “Model Structure,” “Relevant Questions,” “Quality” asdiscussed with respect to other figures. The user may also edit, delete,or mark for future reference the exemplary target objective. The usermay select the dataset's name, in this example “Hotel Satisfaction—NetScore”, to return to a user interface as depicted in FIG. 10 . A usermay hover over or select the plus sign near the dataset name to accessor view additional information about the dataset (for example, filesize, date created, and so forth). The user may select “Promoter” tab toview a user interface as depicted in FIG. 14 .

FIG. 21 is a screen shot depicting one embodiment of an example userinterface that generates a graphical display of the system's calculatedpath for its prediction model for reaching the designated target goalwhere certain data elements hold more or less predictive value relatedto detractors. The user interface also including a graphicalrepresentation of the strength of the predictors, one example of a wayto show this would be to use varying shades of color and varyingcombinations of the data elements. This user interface may be accessedby selecting “Model structure” as it appears in a user interface asdepicted by FIG. 20 . The user interface shows the user a graphicalrepresentation of each of the analyzed predictive questions, andcombinations thereof, and graphically represents strong predictivenessand weak predictiveness, which may be designated by color or shading.This may be referred to as a model graph, and is a visual representationof how the user's variables combine to identify and predict the settarget. In this embodiment, the color and shading of each variablerepresents the relative importance or strength in predicting the settarget, and the super variables (“SV”) represent combined drivervariables or questions that have a strong predictive effect or thetarget objectives. The user may also view the performance of the modelin identifying target respondents, where the model expects to performwith a certain percentage and/or number of results that the modelpredicted are actually positive, predicted negative but is actuallypositive, predicted positive but is actually negative, and predictednegative and is actually negative. The user interface may also providestatistics on the model which may include, but is not limited to,accuracy, precision (for example, predicted target response(s)),efficiency, sensitivity (for example, coverage), specificity, rowsanalyzed, and population percent (for example, exemplary targetresponse(s)). The user may also view specific target details on thisuser interface page. The user may have the option to download the graph.The user may also have the option to move the graph around a particularviewer and/or zoom in and/or out to make the graph appear larger orsmaller.

FIG. 22 is a screen shot depicting one embodiment of an example userinterface that generates a graphical display of specific profiles of themost positive and the most negative characteristics of individuals in adata set pertaining to the detractor analysis. This allows the user tosee which types of profiles are most and least useful in achieving theobjective(s). The exemplary screen shot illustrates the “BestIndividuals Combinations,” which may display a list of single points inthe data set that represent the best individual profiles for thecriteria that would be a good target respondent and the relatedreasoning. The table may show the profiles of the individuals who aremore or less likely to be the best for the user to target to achieve itsdesired objective/goal initially set out for the analysis. The interfacemay also display relevant information related to each individual profilelisted, such as, but not limited to, a set number of the top and bottomprofiles for the most and least likely to be a target respondent, theexemplary target rate as calculated by the PAS, the questions the PASdeems most predictive and the answers associated with those questions,individual target rate, which is the predicted target rate for eachindividual profile, and the ratio to the population, which is theindividual target rate divided by the exemplary target rate. The mostand/or least likely individual profile to be a target respondent may bedesignated by color, such as green or purple, respectively. The user mayhave the ability to download the selected profiles as well as excludedata in the downloaded file.

FIG. 23 is a screen shot depicting one embodiment of an example userinterface, similar to FIG. 19 that generates a graphical representationof the relationship between data element and how they lead to anobjective pertaining to the detractor analysis. This user interface maybe accessed by selecting “Relationships” as it appears in a userinterface as depicted by FIG. 20 , or other similar figures. Therelationships graph visually depicts the related questions, theirdirection, and strength of influence to the initial target as set by theuser. Color and/or shading may be used to indicate the strength of therelationship to the final target, where, for example, the brighter coloris more predictive. The user may also download the graph, move the grapharound a particular viewer, or zoom in and/or out to make the graphappear larger or smaller.

FIG. 24 is a screen shot depicting one embodiment of an example userinterface allowing for the input of custom criteria for analyzing thedata. The user may perform an analysis for a target based on but notlimited to, selecting a target variable, selecting target questionresponse(s), designating a name for the target, adding exclusions (forexample, exclude a particular question from the analysis), selecting asubset of data, edit any statistical parameters (for example,cardinality, confidence level, cost of false positives, and/or cost offalse negatives), and/or deciding whether to include empty target rows.The user may select “Submit Target” (not shown) to begin the system'sprocessing and analysis to create a prediction model. The user may alsoselect “Net Score Analysis” to view a user interface, similar to FIG. 11, which allows for different type of analysis options.

FIG. 25 is a screen shot depicting one embodiment of an example userinterface that generates a graphical display of the summary ofpredictive results based on inputs. Example shows a graphicalrepresentation of the most stable variables for predicting the targetobjective(s). Example also shows a graphical representation of theexemplary target. After a user selects a target and programs the desiredcriteria and selects “Submit Target” in FIG. 29 , this page appears toshow results of the analysis. Information made available to the user mayinclude, but is not limited to, title of test run as previously set,predictive variables, which are the more important questions forpredicting the set target related to the tested set of data, best groupmost likely to match the target response, best individual most likely tomatch the target response, data quality for prediction (for example, anyscale of either 1-10, 0-100%, or 1-5 stars, and so forth), predictiondetails, including the coverage as well the total values the predictionmodel covers over the total number of targets, the factor of efficiencybased on predicted target responses over exemplary target values, orincluding any appropriate or relevant information that would be usefulto the user. The user may select any number of tabs for further analysisor viewing including, but not limited to, the exemplary tabs “Summary,”“Model Structure,” “Relevant Questions”, “Quality,” and/or “Validate.”The user may also select any number of tabs for further analysis orviewing including, but not limited to, the exemplary tabs “Model,”and/or “Action.” Some of these mentioned pages are illustrated infollowing figures. In some embodiments, the user may also have theability to edit, delete, or mark for future reference the exemplarytarget objective. The user may select “Datasets” to return to the pageillustrated in FIG. 8 . The user may select the dataset's name, in thisexample “ExampleDataSet,” to return to a user interface as depicted inFIG. 29 . A user may hover over or select the plus sign near the datasetname to access or view additional information about the dataset (forexample, file size, date created, and so forth).

FIG. 26 is a screen shot depicting one embodiment of an example userinterface that generates a graphical display of predictive details andstatistics of an analysis, also illustrating which data elements aremore or less predictive using color and combination. This user interfacemay be accessed by selecting “Model Structure” as it appears in a userinterface as depicted by FIG. 25 , or other similar figures. The userinterface shows the user a graphical representation of each of theanalyzed predictive questions, and combinations thereof, and showsstrong predictiveness and weak predictiveness, which may be designatedby color or shading. This may be referred to as a model graph, and is avisual representation of how the user's variables combine to identifyand predict the set target. In this embodiment, the color and shading ofeach variable represents the relative importance or strength inpredicting the set target, and the super variables represent combineddriver variables that have predictive effect or the target objective.The user may also view the performance of the model in identifyingtarget respondents, where the model expects to perform with a certainpercentage and/or number of results that the model predicted positiveand is actually positive, predicted negative and is actually positive,predicted positive and is actually negative, and predicted negative andis actually negative. The user interface may also provide statistics onthe model which may include, but is not limited to, accuracy, precision(for example, predicted target response(s)), efficiency, sensitivity(for example, coverage), specificity, rows analyzed, and populationpercent (for example, exemplary target response(s)). The user may alsoview specific target details on this user interface page. The user mayhave the option to download the graph. The user may also have the optionto move the graph around a particular viewer and/or zoom in and/or outto make the graph appear larger or smaller.

FIG. 27 is a screen shot depicting one embodiment of an example userinterface that generates a graphical display of specific profiles of themost positive and the most negative characteristics of individuals in adata set pertaining to the promoter analysis. The exemplary screen shotillustrates the “Best Individuals Combinations,” which may include alist of single points in the data set that represent the best individualprofiles that show who is likely to be a target respondent and why. Thetable may show the profiles of the individuals who are more or lesslikely to be the best for the user to target to achieve its desiredobjective/goal initially set out for the analysis. The interface mayalso display relevant information related to each individual profilelisted, such as, but not limited to, a set number of the top and bottomprofiles for the most and least likely to be a target respondent, theexemplary target rate as calculated by the PAS, the questions the PASdeems most predictive and the answers associated with those questions,individual target rate, which is the predicted target rate for eachindividual profile, and the ratio to the population, which is theindividual target rate divided by the exemplary target rate. The mostand/or least likely individual profile to be a target respondent may bedesignated by color, such as green or purple, respectively. The user mayhave the ability to download the selected profiles as well as excludedata in the downloaded file.

FIG. 28 is a screen shot depicting one embodiment of an example userinterface that generates a graphical display of the relationship betweendata points and how they lead to an objective. This user interface maybe accessed by selecting “Relevant Variables” as it appears in a userinterface as depicted by FIG. 25 , or other similar figures. Therelationships graph visually depicts the related questions, theirdirection, and strength of influence to the initial target as set by theuser. Color and/or shading may be used to indicate the strength of therelationship to the final target, where, for example, the brighter coloris more predictive. The user may also download the graph, move the grapharound a particular viewer, or zoom in and/or out to make the graphappear larger or smaller.

FIG. 29 is a screen shot depicting one embodiment of an example userinterface for setting a goal based on focused elements of data andrecommendations. The user may select the focused objective delta, or anamount the user would like the net score to change for the selectedgoal, the net score (for example, +5 in this embodiment) or input theuser's own desired change to the score using varying methods (forexample, typing, dragging a slider, or by voice). Thus, the userinterface allows the user to not only indicate the type of change but adesired amount of change. The user may exclude certain questions thesystem designated as predictive to the analysis. A user may choose toexclude information that the user cannot affect through any action ofits own, or any affect would be too cost-prohibitive, or out of thescope of the user's abilities. For example, the user may exclude age,gender, or race since those are factors the user cannot change in thecustomer data, but keep in monthly price, staff service level, and classsize since those are factors the user could change. In this example, theuser excludes two variables, “relationship” and “marital-status.” Theuser may select “Apply” to send the information to the system tocalculate its analysis and provide recommendation(s) to the user.Alternatively, the system can run the analysis without the user's activeparticipation. The user may also reset the analysis criteria todefaults. The system's recommendation(s) may show information to theuser that would indicate by what percentage to increase or decrease thetypes of answers received by a specific predictive question. Color maybe used as an indicator for whether to increase (for example, green) ordecrease (for example, blue or purple) a specific answer to a predictivequestion. These answers may be influenced by actions in the physicalworld such as cleaning the facilities or implementing more effectivetraining for staff.

FIG. 30A is a screen shot depicting one embodiment of an example userinterface for the entry of a single data point prediction based oncustomizable criteria and a previously generated model. A test isperformed with the previously generated prediction model created by thePAS on the new single data point entered. The output results may appearsomewhere on the page, with each additional test also appearing in anyparticular order or sorting arrangement. The results may also list theinput prediction criteria for the user's reference. The user may alsodiscard at least one test to all tests if the user chooses. The outputresults may include whether the tested data returns a true or falsevalue based on whether the respondent meets the set target criteria (forexample, would lead to a car sale). The output results also may includea confidence level indicating the statistical certainty of the model asapplied to the respective input data. The user may also see whatpredictor it will be validating by name.

FIG. 30B is a screen shot depicting one embodiment of an example userinterface for the entry of bulk data point prediction based oncustomizable criteria and a previously generated model. By selecting“Bulk Prediction,” a user is directed to a page to upload or providedata to the PAS in the form of a file including multiple responses tothe prediction questions in order to get a prediction for eachrespondent in a row. The user may upload or provide data to the systemby, (1) dragging and dropping a file including data, (2) selecting the“Choose a File” button to manually select the data file from a local ornetwork storage, (3) uploading data via a URL hyperlink that points to adataset that may be stored remotely, and provide any necessaryauthentication for access, or (4) using any other appropriate method.The data the system receives may be in a format the system supports.Formats may include, comma-separated values (“CSV”), tab separatedvalues (“TSV”), a compressed file format, such as ZIP, that includeseither CSV or TSV, or both, or other supported formats. In someembodiments, the user may also specify the format that the systemoutputs result, such as a “compact” format, only providing essentialdata, or “full” format, providing all, or nearly all, data available.The results indicate why the individual received the given prediction byranking the importance of each response to the prediction questions. Theuser may also see what predictor it will be validating by name. The PASmay also export by saving to a cloud storage endpoint.

FIG. 30C is a screen shot depicting one embodiment of an example userinterface for the entry of additional data to test or retrain aprediction model. By selecting “Validate,” a user is directed to a pageto use additional data to test the model and to check or calibrate themodel's performance. The user may also use this interface to retrain thepredictor by appending uploaded new data to the original dataset. Theuser may upload or provide data to the system by, (1) dragging anddropping a file including data, (2) selecting the “Choose a File” buttonto manually select the data file from a local or network storage, (3)uploading data via a URL hyperlink that points to a dataset that may bestored remotely, and provide any necessary authentication for access, or(3) using any other appropriate method. The system may accept a varietyof formats including, comma-separated values (“CSV”), tab separatedvalues (“TSV”), a compressed file format, such as ZIP, that includeseither CSV or TSV, or both, or other supported formats. The user mayalso see what predictor it will be validating by name.

IX. Semantic Analysis

In some embodiments, a semantic analysis may include elements ofnon-structured data that are classified as “positive” or “negative” bytheir corresponding association with positive or negative businessoutcomes. The language elements may be classified as positive ornegative in terms of the specific preconfigured objective the languageelements represent. In the semantic analysis, a prescription regarding apreconfigured objective can be enriched with variants of concreteimprovement actions. In some embodiments, the prescription generated isconcrete in pointing to the advised practical beneficial changes and/orin an assessment to what degree those changes would be sufficient withrespect to the preconfigured objective.

FIG. 31A is a block diagram depicting one embodiment of an architecture3100 for analyzing collections of structured and non-structured data bya PAS of a computing environment that includes a Database 3108, aDependence Circuit 3101, and a Semantic Circuit 3103, according to oneembodiment. The architecture 3100 shown in FIGS. 31A and 31B includecomponents and devices similar to FIGS. 1A and 1B (for example, userdevice 104, feedback data 102 or data sets from the user's customers oremployees, a third party data collection system 106 with a database 107,a PAS 108, and an application interface 110), as well as thefunctionality offered by such components and devices.

A. Exemplary High-Level Data Flow

FIG. 31B is one embodiment of an exemplary data flow using the computingenvironment of FIG. 31A. The architecture includes a database 3108 thatincludes a collection of data in the form of multiple instances orrecords. Each record includes a structural part or structured data 3104(for example, measurements or categorical labels), a natural languagepart or natural language data 3106 (for example, some measurements andtechnical comments, or medical lab results and doctor's diagnosticconclusions), and a definition of a Boolean target to predict or Booleanresponse 3102 (for example, a failure of a designated manufactured uniton a production line).

In one embodiment, a polarity analysis data flow or dependence analysis3101 starts with links 1 and 2 by transmitting a Boolean response 3102comprising a user-defined target and structured data 3104 to a PAS 3112.Then the PAS 3112 generates a predictive model for the user-definedtarget and, in link 3, sends the generated predictive model to a modelrepository 3116. The database 3108 may also electronically communicate(shown as link 4) with an accumulator of predictions 3118, whichretrieves or receives some or all data records in the database 3108. Theaccumulator or storage of predictions 3118 also retrieves or receivesoutputs (link 5) (for example, predictions) from the generatedpredictive model. Additionally, the database 3108 may electronicallycommunicate with a language data preprocessor 3110 (link 6), whichidentifies lemmas (groups of words having a stable semantic meaning, forexample). In links 7 and 8, the language data preprocessor 3110 andstorage of predictions 3118 transmit (or allow retrieval of) identifiedlemmas and predictions via a lemma classification module 3120. The lemmaclassification module 3120 classifies lemmas as positive or negativebased on the received or retrieved predictions from the storage ofpredictions 3118. The results of the classification are sent from thelemma classification module 3120 (link 9) to an output module 3122,which displays textual labels associated with the records from thedatabase 3108 along with their corresponding polarity.

A semantic analysis 3103 portion then begins with the use of the earliercreated predictive model for creation of a relation graph 3128, G1, (forexample, the dependence circuit received or retrieved in link 10). Therelation graph 3128 shows associations between model inputs based onstrength of the associations. The result is stored in relationrepository 3130 in link 11. Then, the polarity classification retrievedor received in link 12 from the output module 3122 is used to create asecond graph, G2, showing associations between model inputs andclassified lemmas. The system connects graphs G1 and G2 (links 13 and14) to form an “extended graph” referred to as a semantic circuit. Theprocess ends with an interpreter module receiving or retrieving theextended graph in link 15 to generate a report, user interface displayinstructions, or display providing associative or causal interpretationsof discovered structural relations by natural language. In someembodiments, such associative or causal interpretations can bedetermined by using lemmas associated with nodes of G1. The semanticanalysis 3103 portion of the analysis can provide more specific adviceof action, explanation of context of business events, and search forcore reasons of events in one or more particular business processes.

B. Prescriptive Analytics System (“PAS”) with Relation Module andSemantic Analytics Engine

FIG. 32A, similar to FIG. 2 , is a block diagram showing one embodiment3200 of a PAS 3210 and its interaction with a set of input data 201 andgeneration of output 230. The PAS 3210 is one embodiment of the PAS 3112of FIGS. 31A and 31B. The components and relationship between thecomponents of the PAS 3210 in FIG. 32 are similar to the embodiment 200depicted and described in relation to FIG. 2 , which is described inmore detail herein. For example, the PAS 3210 of FIG. 32 is similar tothe PAS 210 of FIG. 2 . However, FIG. 32A and embodiment 3200 include asemantic analytics engine 3227 and a predictive analytics engine 3228which includes a relation module.

Similar to the exemplary predictive analytics engine 228 in FIG. 2 , theexemplary predictive analytics engine 3228 in FIG. 32A is configured toanalyze structured data in view of a selected objective to identifyfactors or groups of factors that have the most predictive effect on theobjective and generate a corresponding predictive model. The predictiveanalytics engine 228 may also be configured to regenerate or update thepredictive model based on new or updated feedback data. Further, in someembodiments, the predictive analytics engine 3228 can be enhanced with arelation module that can generate a graph of relations between eachvariable of inputted structured data 206. Links in the graph are builtaccordingly to associations between variables representing the strengthof such associations and represent a dependence circuit modeling thestructured data 206.

The semantic analytics engine 3227 extends the dependence circuit orgraph of relations generated by the predictive analytics engine 3228with additional links to natural strongly related language elements. Thelanguage elements are characterized in a specific and granular way as aconcrete specification of context, from which the dependence circuitinitially arose. The extended graph can be referred to as a semanticcircuit. It can be used for more specific advice of action and forcausal analysis of events in business process (for example, finding corereasons of failures on the production line).

FIG. 32B is a block diagram showing one embodiment of a semanticsanalytics engine 3227 which includes an input relation graph generationsystem 3227 a, a relation repository 3227 b, an input/lemma relationgraph generation system 3227 c, a connection system 3227 d, and aninterpreter system 3227 e.

In some embodiments, the input relation graph generation system 3227 isconfigured to, or includes instructions that when executed, determineassociations among the model inputs, generate a relation graph, andstore the relation graph in the relation repository 3227 b.

In some embodiments, the input/lemma relation graph generation system3227 c is configured to, or includes instructions that when executed,determine associations among the model inputs and the classified lemmas,generate an input/lemma relation graph, and store the input/lemmarelation graph in the relation repository 3227 b.

In some embodiments, the connection system 3227 d is configured to, orincludes instructions that when executed, connect one or more nodes ofthe relation graph with one or more nodes of the input/lemma relationgraph to generate an extended graph and store the extended graph in therelation repository 3227 b.

In some embodiments, the interpreter system 3227 e is configured to, orincludes instructions that when executed, generate a report, a display,in instructions for displaying associative or causal interpretations ofrelations within the extended graph. Such associative or causalinterpretations of relations may be represented by one or more of fixedlanguage, natural language, graphs, charts, and so forth.

C. Example of a Semantic Analysis

FIG. 33 is one embodiment of a block diagram which illustrates a logicalflow diagram for conducting a prescriptive analysis which may utilizevarious components of the PAS 3210 including, for example,communications module, predictive analytics engine, polarity analysisengine, and semantic analysis Engine.

Blocks 304-322 are similar to the same numbered blocks in FIG. 3 , whichdescriptions are included herein in relation to FIG. 3 .

In block 336, the semantic analysis engine then determines relationshipsbetween variables of correlated structured data and of filteredstandardized lemma data. The polarity classification is used to create agraph G2 showing associations between model inputs and classifiedlemmas.

In block 338, the semantic analysis engine determines the relationshipsbetween variables of the correlated structured data based on thepredictive model generated in block 310. In block 340, the semanticanalysis engine uses the determined relationships to generate a relationgraph G1 (for example, a dependence circuit), which shows theassociations between model inputs based on strength of the associations.

In block 342, the semantic analysis engine generates an extended graph(for example, a semantic circuit) by using the relation graph G1 and thegraph G2.

In block 344, the semantic analysis engine analyzes the extended graphor semantic circuit to determine associative or causal interpretationsof discovered structural relations by natural language means. In someembodiments, such associative or causal interpretations can bedetermined by using lemmas associated with nodes of G1.

In block 346, based on the analysis in block 344, the semantic analysisengine generates a report, instructions for display, or displayproviding recommendations to present to the user that the user couldtake to better meet the objectives from block 306.

It is recognized that some blocks may execute in a difference order orin parallel with other blocks. For example, blocks 336 and 338 may beexecuted in parallel, or blocks 338 can be executed before block 336.

D. Processing and Analysis of Data

FIG. 34 schematically illustrates a logical flow diagram for oneembodiment of an example process for analyzing data and generatingrecommendations using a polarity analysis enhanced with a relationmodule and a semantic analysis. FIG. 34 is an additional embodiment ofblock 412 in FIG. 4 such that after the PAS processes the data in FIG. 5, FIG. 34 encompasses one of the events or processes after or during theevents of FIG. 5 .

Blocks 602-608 are similar to the blocks 302-608 in FIG. 6 , which isdescribed in more detail herein.

In block 612, the semantic analysis engine determines relationshipsbetween variables of correlated structured data and of filteredstandardized lemma data. The polarity classification is used to create agraph G2 showing associations between model inputs and classifiedlemmas.

In block 614, the semantic analysis engine determines the relationshipsbetween variables of the correlated structured data based on thepredictive model generated in block 310.

In block 616, the semantic analysis engine uses the determinedrelationships to generate a relation graph G1 (for example, a dependencecircuit), which shows the associations between model inputs based onstrength of the associations.

In block 618, the semantic analysis engine uses the determinedrelationships between variables of the correlated structured data andfiltered lemma data to generate a model input/lemmas graph and stores itin a repository. Then the PAS generates an extended graph (for example,a semantic circuit) by using the relation graph G1 and the graph G2.

In block 620, the PAS analyzes the extended graph or semantic circuit todetermine associative or causal interpretations of discovered structuralrelations by natural language means. In some embodiments, suchassociative or causal interpretations can be determined by using lemmasassociated with nodes of G1.

In block 622, the PAS, based on the analysis in block 620, the semanticanalysis engine generates a report or display providing recommendationsto present to the user that the user could take to better meet theobjectives from block 404 in FIG. 4 .

It is recognized that a variety of embodiments may be used to conductthe analyses and that some of the processes above may be combined,separated into sub-blocks, and rearranged to run in a different orderand/or in parallel. In addition, in some embodiments, different blocksmay execute on various components of the PAS including the semanticanalysis engine.

E. Example Dependence Circuit and Semantic Circuit Graphs

FIGS. 35A and 35B are screen shots depicting embodiments of generatedgraphs using a dependence circuit and a semantic circuit based on surveyresults. For example, the survey results are based on a fictitious yogastudio with preconfigured variables, where an objective was programed totarget the willingness of survey respondents to recommend the particularyoga studio (for example, 8-10 on a 1-10 scale). A data set of responsesincludes of (1) an evaluation of quality aspects ranked on a scale of 1to 10, and (2) an option to describe the respondent's impression in afree text style. The examples depicted in FIGS. 35A and 35B do not limithow a dependence circuit or semantic circuit is generated orillustrated. There can be many versions of the dependence circuit orsemantic circuit that can be generated for various examples.

FIG. 35A is a screen shot depicting one embodiment of a generated graph,illustrating a graph generated by a process using a dependence circuit.The generated graph in FIG. 35A is a relation graph that shows theinfluence of topics covered by a specific question on topics related toother questions asked in the survey. For example, a polarity analysiscan automatically select key words, which reflect issues expressed inthe free-text part, and then links the key words to positive or negativeas it relates to a predictive model anticipating the target result (forexample, a recommendation score of 8-10, and not a recommendation scoreof 1-7).

FIG. 35B is a screen shot depicting one embodiment of an generatedgraph, illustrating a graph generated by a process using a semanticcircuit based on FIG. 35A. The semantic extension of polarity analysis(for example, polarity semantics) can supplement the graph depicted inFIG. 35A by further determining which question the polarity keywords aremost strongly related. For example, a result of a polarity semantics isdepicted in FIG. 35B, which illustrates an estimate of how keywordsdetermined in the polarity analysis associated with FIG. 35A relate todifferent issues to establish more refined or granular topics. Thesetopics can be further refined from the context of services andinteractions of respondents to the survey and conveyed to the yogastudio management or staff.

X. Computing System

In some embodiments, any of the systems, servers, or componentsreferenced herein may take the form of a computing system as shown inFIG. 36 which illustrates a block diagram of one embodiment of a type ofcomputing system 802. The exemplary computing system 802 includes acentral processing unit (“CPU”) 810, which may include one or moreconventional microprocessors that comprise hardware circuitry configuredto read computer-executable instructions and to cause portions of thehardware circuitry to perform operations specifically defined by thecircuitry. The computing system 802 may also include a memory 812, suchas random access memory (“RAM”) for temporary storage of information andread only memory (“ROM”) for permanent storage of information, which maystore some or all of the computer-executable instructions prior to beingcommunicated to the processor for execution. The computing system mayalso include one or more mass storage devices 804, such as a hard drive,diskette, CD-ROM drive, a DVD-ROM drive, or optical media storagedevice, that may store the computer-executable instructions forrelatively long periods, including, for example, when the computersystem is turned off. Typically, the modules of the computing system areconnected using a standard based bus system. In different embodiments,the standard based bus system could be Peripheral Component Interconnect(“PCI”), Microchannel, Small Computer System Interface (“SCSI”),Industrial Standard Architecture (“ISA”) and Extended ISA (“EISA”)architectures, for example. In addition, the functionality provided forin the components and modules of computing system may be combined intofewer components and modules or further separated into additionalcomponents and modules. The illustrated structure of the computingsystem 802 may also be used to implement other computing components andsystems described in the disclosure. It is recognized that thecomponents discussed herein may be implemented as different types ofcomponents. For example, a server may be implemented as a moduleexecuting on a computing device, a mainframe may be implemented on anon-mainframe server, a server or other computing device may beimplemented using two or more computing devices, and/or variouscomponents could be implemented using a single computing device.

Also, it is recognized that a variety of embodiments may be used andthat some of the blocks in FIG. 36 may be combined, separated intosub-blocks, and rearranged to run in a different order and/or inparallel.

In one embodiment, the computing system 802 is a server, a workstation,a mainframe, a minicomputer. In other embodiments, the system may be apersonal computer that is IBM, Macintosh, or Linux/Unix compatible, alaptop computer, a tablet, a handheld device, a mobile phone, a smartphone, a smart watch, a personal digital assistant, a car system, atablet or other user device. Servers may include a variety of serverssuch as database servers (for example, Oracle, DB2, Informix, MicrosoftSQL Server, MySQL, or Ingres), application servers, data loader servers,or web servers. In addition, the servers may run a variety of softwarefor data visualization, distributed file systems, distributedprocessing, web portals, enterprise workflow, form management, and soforth.

The computing system 802 may be generally controlled and coordinated byoperating system software, such as Windows 95, Windows 98, Windows NT,Windows 2000, Windows XP, Windows Vista, Windows 7, Windows 8, Windows10, Unix, Linux, SunOS, Solaris, Maemo, MeeGo, BlackBerry Tablet OS,Android, webOS, Sugar, Symbian OS, MAC OS X, or iOS or other operatingsystems. In other embodiments, the computing system 802 may becontrolled by a proprietary operating system. Conventional operatingsystems control and schedule computer processes for execution, performmemory management, provide file system, networking, I/O services, andprovide a user interface, such as a graphical user interface (“GUI”),among other things.

The exemplary computing system 802 includes one or more commonlyavailable input/output (“I/O”) devices and interfaces 808, such as akeyboard, mouse, touchpad, speaker, microphone, or printer. In oneembodiment, the I/O devices and interfaces 808 include one or moredisplay device, such as a touchscreen, display or monitor, which allowsthe visual presentation of data to a user. More particularly, a displaydevice provides for the presentation of GUIs, application software data,and multimedia presentations, for example. The central processing unit810 may be in communication with a display device that is configured toperform some of the functions defined by the computer-executableinstructions. For example, some of the computer-executable instructionsmay define the operation of displaying to a display device, an imagethat is like one of the screen shots included in this application. Thecomputing system may also include one or more multimedia devices 806,such as speakers, video cards, graphics accelerators, and microphones,for example. A skilled artisan would appreciate that, in light of thisdisclosure, a system, including all hardware components, such as thecentral processing unit 810, display device, memory 812, and massstorage device 804 that are necessary to perform the operationsillustrated in this application, is within the scope of the disclosure.

In the embodiment of FIG. 36 , the I/O devices and interfaces provide acommunication interface to various external devices and systems. Thecomputing system may be electronically coupled to a network 818, whichcomprises one or more of a LAN, WAN, the Internet, or cloud computingnetworks, for example, via a wired, wireless, or combination of wiredand wireless, communication links. The network communicates with varioussystems or other systems via wired or wireless communication links 820,as well as various data sources 822.

Information may be provided to the computing system 802 over the networkfrom one or more data sources including, for example, user 104 or surveydatabase 106. In addition to the sources that are illustrated in FIGS.1A and 1B, the network may communicate with other data sources or othercomputing devices such as a third party survey provider system ordatabase, for example. The data sources may include one or more internalor external data sources. In some embodiments, one or more of thedatabases or data sources may be implemented using a relationaldatabase, such as Sybase, Oracle, CodeBase and Microsoft® SQL Server aswell as other types of databases such as, for example, a flat filedatabase, an entity-relationship database, and object-oriented database,or a record-based database.

In the embodiment of FIG. 36 , the computing system 802 also includes aPAS 814, which may be executed by the CPU 810, to run one or more of theprocesses discussed herein. This system may include, by way of example,components, such as software components, object-oriented softwarecomponents, class components, task components, processes, functions,attributes, procedures, subroutines, segments of program code, drivers,firmware, microcode, circuitry, data, databases, data structures,tables, arrays, or variables. In one embodiment, the PAS 814 may includeone or more of the modules shown in block 210 in FIG. 2 .

Embodiments can be implemented such that all functions illustratedherein are performed on a single device, while other embodiments can beimplemented in a distributed environment in which the functions arecollectively performed on two or more devices that are in communicationwith each other. Moreover, while the computing system has been used todescribe one embodiment of a PAS 210, it is recognized that the user orcustomer systems may be implemented as computing systems as well.

In general, the word “module,” as used herein, refers to logic embodiedin hardware or firmware, or to a collection of software instructions,possibly having entry and exit points, written in a programminglanguage, such as, for example, Java, Lua, C or C++. A software modulemay be compiled and linked into an executable program, installed in adynamic link library, or may be written in an interpreted programminglanguage such as, for example, BASIC, Perl, or Python. It will beappreciated that software modules may be callable from other modules orfrom themselves, or may be invoked in response to detected events orinterrupts. Software instructions may be embedded in firmware, such asan EPROM. It will be further appreciated that hardware modules may becomprised of connected logic units, such as gates and flip-flops, or maybe comprised of programmable units, such as programmable gate arrays orprocessors. The modules described herein are preferably implemented assoftware modules, but may be represented in hardware or firmware.Generally, the modules described herein refer to logical modules thatmay be combined with other modules or divided into sub-modules despitetheir physical organization or storage.

It is recognized that the term “remote” may include systems, data,objects, devices, components, or modules not stored locally, that arenot accessible via the local bus. Thus, remote data may include a systemthat is physically stored in the same room and connected to thecomputing system via a network. In other situations, a remote device mayalso be located in a separate geographic area, such as, for example, ina different location, country, and so forth.

XI. Additional Embodiments

It is to be understood that not necessarily all objects or advantagesmay be achieved in accordance with any particular embodiment describedherein. Thus, for example, those skilled in the art will recognize thatcertain embodiments may be configured to operate in a manner thatachieves or optimizes one advantage or group of advantages as taughtherein without necessarily achieving other objects or advantages as maybe taught or suggested herein.

All of the processes described herein may be embodied in, and fullyautomated via, software code modules executed by a computing system thatincludes one or more general purpose computers or processors. The codemodules may be stored in any type of non-transitory computer-readablemedium or other computer storage device. Some or all the methods mayalternatively be embodied in specialized computer hardware. In addition,the components referred to herein may be implemented in hardware,software, firmware or a combination thereof.

Many other variations than those described herein will be apparent fromthis disclosure. For example, depending on the embodiment, certain acts,events, or functions of any of the algorithms described herein can beperformed in a different sequence, can be added, merged, or left outaltogether (for example, not all described acts or events are necessaryfor the practice of the algorithms). Moreover, in certain embodiments,acts or events can be performed concurrently, for example, throughmulti-threaded processing, interrupt processing, or multiple processorsor processor cores or on other parallel architectures, rather thansequentially. In addition, different tasks or processes can be performedby different machines and/or computing systems that can functiontogether.

The various illustrative logical blocks, modules, and algorithm elementsdescribed in connection with the embodiments disclosed herein can beimplemented as electronic hardware, computer software, or combinationsof both. To clearly illustrate this interchangeability of hardware andsoftware, various illustrative components, blocks, modules, and elementshave been described above generally in terms of their functionality.Whether such functionality is implemented as hardware or softwaredepends upon the particular application and design constraints imposedon the overall system. The described functionality can be implemented invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the disclosure.

The various illustrative logical blocks and modules described inconnection with the embodiments disclosed herein can be implemented orperformed by a machine, such as a general purpose processor, a digitalsignal processor (“DSP”), an application specific integrated circuit(“ASIC”), a field programmable gate array (“FPGA”) or other programmablelogic device, discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. A general purpose processor can be a microprocessor,but in the alternative, the processor can be a controller,microcontroller, or state machine, combinations of the same, or thelike. A processor can include electrical circuitry configured to processcomputer-executable instructions. In another embodiment, a processorincludes an FPGA or other programmable devices that performs logicoperations without processing computer-executable instructions. Aprocessor can also be implemented as a combination of computing devices,for example, a combination of a DSP and a microprocessor, a plurality ofmicroprocessors, one or more microprocessors in conjunction with a DSPcore, or any other such configuration. Although described hereinprimarily with respect to digital technology, a processor may alsoinclude primarily analog components. For example, some, or all, of thesignal processing algorithms described herein may be implemented inanalog circuitry or mixed analog and digital circuitry. A computingenvironment can include any type of computer system, including, but notlimited to, a computer system based on a microprocessor, a mainframecomputer, a digital signal processor, a portable computing device, adevice controller, or a computational engine within an appliance, toname a few.

The elements of a method, process, or algorithm described in connectionwith the embodiments disclosed herein can be embodied directly inhardware, in a software module stored in one or more memory devices andexecuted by one or more processors, or in a combination of the two. Asoftware module can reside in RAM memory, flash memory, ROM memory,EPROM memory, EEPROM memory, registers, hard disk, a removable disk, aCD-ROM, or any other form of non-transitory computer-readable storagemedium, media, or physical computer storage known in the art. An examplestorage medium can be coupled to the processor such that the processorcan read information from, and write information to, the storage medium.In the alternative, the storage medium can be integral to the processor.The storage medium can be volatile or nonvolatile. The processor and thestorage medium can reside in an ASIC. The ASIC can reside in a userterminal. In the alternative, the processor and the storage medium canreside as discrete components in a user terminal.

Conditional language such as, among others, “can,” “could,” “might” or“may,” unless specifically stated otherwise, are otherwise understoodwithin the context as used in general to convey that certain embodimentsinclude, while other embodiments do not include, certain features,elements and/or steps. Thus, such conditional language is not generallyintended to imply that features, elements and/or steps are in any wayrequired for one or more embodiments or that one or more embodimentsnecessarily include logic for deciding, with or without user input orprompting, whether these features, elements and/or steps are included orare to be performed in any particular embodiment.

Disjunctive language such as the phrase “at least one of X, Y, or Z,”unless specifically stated otherwise, is otherwise understood with thecontext as used in general to present that an item, term, and so forth,may be either X, Y, or Z, or any combination thereof (for example, X, Y,and/or Z). Thus, such disjunctive language is not generally intended to,and should not, imply that certain embodiments require at least one ofX, at least one of Y, or at least one of Z to each be present.

Any process descriptions, elements or blocks in the flow diagramsdescribed herein and/or depicted in the attached figures should beunderstood as potentially representing modules, segments, or portions ofcode which include one or more executable instructions for implementingspecific logical functions or elements in the process. Alternateimplementations are included within the scope of the embodimentsdescribed herein in which elements or functions may be deleted, executedout of order from that shown, or discussed, including substantiallyconcurrently or in reverse order, depending on the functionalityinvolved as would be understood by those skilled in the art.

Unless otherwise explicitly stated, articles such as “a” or “an” shouldgenerally be interpreted to include one or more described items.Accordingly, phrases such as “a device configured to” are intended toinclude one or more recited devices. Such one or more recited devicescan also be collectively configured to carry out the stated recitations.For example, “a processor configured to carry out recitations A, B andC” can include a first processor configured to carry out recitation Aworking in conjunction with a second processor configured to carry outrecitations B and C.

It should be emphasized that many variations and modifications may bemade to the above-described embodiments, the elements of which are to beunderstood as being among other acceptable examples. All suchmodifications and variations are intended to be included herein withinthe scope of this disclosure and protected by the following.

What is claimed is:
 1. A system comprising: one or more electronicdatabases storing: a set of response data comprising a structured dataset and free text data; an objective corresponding to the set ofresponse data; and one or more hardware processors configured to executecomputer-executable instructions in order to: access a correlatedstructured data set based at least in part on the structured data setand key predictive factors that are correlated to the objective; apply apredictive model that is based at least in part on the structured dataset and the first objective to generate a first electronic graph datadependency structure that represents relationships among at least aportion of variables of the correlated structured data set; generate asecond electronic graph data dependency structure of associations amongpredictive model inputs and polarity values associated with at least aportion of the free text data, wherein the polarity values indicate thatthe associated free text data is associated with a degree of impact onone or more outcomes; generate an extended electronic graph datadependency structure based at least in part on the first and secondelectronic graph data dependency structures; and generate arecommendation based at least in part on the objective and the extendedelectronic graph data dependency structure.
 2. The system of claim 1,wherein the set of response data is based at least in part on anaggregated customer feedback data set.
 3. The system of claim 1, whereinthe objective is selected by a user.
 4. The system of claim 1, whereinthe key predictive factors that are correlated to the first objectiveindicate one or more behavior patterns associated with the objective. 5.The system of claim 4, wherein the predictive model indicates one ormore behavior patterns associated with an objective.
 6. The system ofclaim 1, wherein the relationships among at least a portion of variablesof the correlated structured data set are based at least in part on thestrength of an association among each of the variables of the correlatedstructured data set.
 7. The system of claim 1, wherein the one or morehardware processors are further configured to executecomputer-executable instructions in order to: determine associativeinterpretations or causal interpretations of discovered relationshipsbetween each of the variables of the correlated structured data set. 8.The system of claim 2, wherein the recommendation is based at least inpart on the associative or causal interpretations.
 9. The system ofclaim 1, wherein the one or more hardware processors are furtherconfigured to execute computer-executable instructions in order to:generate a data packet that includes a graphical representation of atleast a subset of the polarity values that includes a graphicalrepresentation of the extended graph, the data packet configured fordisplay on a remote computing device.
 10. The system of claim 1, whereinthe one or more hardware processors are further configured to executecomputer-executable instructions in order to: generate a data packetthat includes a graphical representation of at least a subset of thepolarity values that includes a graphical representation of whether aterm in the at least a subset of the polarity values corresponds to anegative sentiment or a positive sentiment, the data packet configuredfor display on a remote computing device.
 11. The system of claim 1,wherein the one or more hardware processors are further configured toexecute computer-executable instructions in order to: generate a datapacket that includes a graphical representation of at least a subset ofthe polarity values that includes a graphical representation of thefrequency and strength of terms in the subset, the data packetconfigured for display on a remote computing device.
 12. The system ofclaim 1, wherein the one or more hardware processors are furtherconfigured to execute computer-executable instructions in order to:generate a data packet that includes a graphical representation of atleast a subset of the polarity values that includes a graphicalrepresentation of whether a term in the subset corresponds to a negativesentiment or a positive sentiment and the frequency and strength ofterms in the subset, the data packet configured for display on a remotecomputing device.
 13. A method comprising: accessing a correlatedstructured data set based at least in part on the structured data setand key predictive factors that are correlated to an objective, whereinthe objective corresponds to a set of response data comprising astructured data set and free text data; applying a predictive model thatis based at least in part on the structured data set and the firstobjective to generate a first electronic graph data dependency structurethat represents relationships among at least a portion of variables ofthe correlated structured data set; generating a second electronic graphdata dependency structure of associations among predictive model inputsand polarity values associated with at least a portion of the free textdata, wherein the polarity values indicate that the associated free textdata is associated with a degree of impact on one or more outcomes;generating an extended electronic graph data dependency structure basedat least in part on the first and second electronic graph datadependency structures; and generating a recommendation based at least inpart on the objective and the extended electronic graph data dependencystructure.
 14. The method of claim 13, wherein the set of response datais based at least in part on an aggregated customer feedback data set.15. The method of claim 13, wherein the key predictive factors that arecorrelated to the first objective indicate one or more behavior patternsassociated with the objective.
 16. The method of claim 13, wherein therelationships among at least a portion of variables of the correlatedstructured data set are based at least in part on the strength of anassociation among each of the variables of the correlated structureddata set.
 17. Non-transitory computer storage having stored thereon acomputer program, the computer program including executable instructionsthat instruct a computer system to at least: access a correlatedstructured data set based at least in part on the structured data setand key predictive factors that are correlated to an objective, whereinthe objective corresponds to a set of response data comprising astructured data set and free text data; apply a predictive model that isbased at least in part on the structured data set and the firstobjective to generate a first electronic graph data dependency structurethat represents relationships among at least a portion of variables ofthe correlated structured data set; generate a second electronic graphdata dependency structure of associations among predictive model inputsand polarity values associated with at least a portion of the free textdata, wherein the polarity values indicate that the associated free textdata is associated with a degree of impact on one or more outcomes;generate an extended electronic graph data dependency structure based atleast in part on the first and second electronic graph data dependencystructures; and generate a recommendation based at least in part on theobjective and the extended electronic graph data dependency structure.18. The non-transitory computer storage of claim 17, wherein the set ofresponse data is based at least in part on an aggregated customerfeedback data set.
 19. The non-transitory computer storage of claim 17,wherein the key predictive factors that are correlated to the firstobjective indicate one or more behavior patterns associated with theobjective.
 20. The non-transitory computer storage of claim 17, whereinthe relationships among at least a portion of variables of thecorrelated structured data set are based at least in part on thestrength of an association among each of the variables of the correlatedstructured data set.